autopep8 and import fix

* make acer use vecframestack

* acer passes mnist test with 20k steps

* acer with non-image observations and tests

* flake8

* test acer serialization with non-recurrent policies

.benchmark_pattern

@@ -0,0 +1 @@
+

.gitignore

@@ -34,5 +34,3 @@ src
 .cache
 
 MUJOCO_LOG.TXT
-
-

.travis.yml

@@ -10,5 +10,5 @@ install:
     - docker build . -t baselines-test
 
 script:
-    - flake8 --select=F baselines/common
-    - docker run baselines-test pytest
+    - flake8 . --show-source --statistics
+    - docker run baselines-test pytest -v .

Dockerfile

@@ -1,20 +1,25 @@
 FROM ubuntu:16.04
 
-RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake
+RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
 ENV CODE_DIR /root/code
 ENV VENV /root/venv
 
-COPY . $CODE_DIR/baselines
 RUN \
     pip install virtualenv && \
     virtualenv $VENV --python=python3 && \
     . $VENV/bin/activate && \
-    cd $CODE_DIR && \
-    pip install --upgrade pip && \
-    pip install -e baselines && \
-    pip install pytest
+    pip install --upgrade pip
 
 ENV PATH=$VENV/bin:$PATH
+
+COPY . $CODE_DIR/baselines
 WORKDIR $CODE_DIR/baselines
 
+# Clean up pycache and pyc files
+RUN rm -rf __pycache__ && \
+    find . -name "*.pyc" -delete && \
+    pip install tensorflow && \
+    pip install -e .[test]
+
+
 CMD /bin/bash

README.md

@@ -15,7 +15,7 @@ sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zli
 ```
     
 ### Mac OS X
-Installation of system packages on Mac requires [Homebrew](https://brew.sh). With Homebrew installed, run the follwing:
+Installation of system packages on Mac requires [Homebrew](https://brew.sh). With Homebrew installed, run the following:
 ```bash
 brew install cmake openmpi
 ```
@@ -38,20 +38,27 @@ More thorough tutorial on virtualenvs and options can be found [here](https://vi
 
 
 ## Installation
-Clone the repo and cd into it:
-```bash
-git clone https://github.com/openai/baselines.git
-cd baselines
-```
-If using virtualenv, create a new virtualenv and activate it
-```bash
-    virtualenv env --python=python3
-    . env/bin/activate
-```
-Install baselines package
-```bash
-pip install -e .
-```
+- Clone the repo and cd into it:
+    ```bash
+    git clone https://github.com/openai/baselines.git
+    cd baselines
+    ```
+- If you don't have TensorFlow installed already, install your favourite flavor of TensorFlow. In most cases, 
+    ```bash 
+    pip install tensorflow-gpu # if you have a CUDA-compatible gpu and proper drivers
+    ```
+    or 
+    ```bash
+    pip install tensorflow
+    ```
+    should be sufficient. Refer to [TensorFlow installation guide](https://www.tensorflow.org/install/)
+    for more details. 
+
+- Install baselines package
+    ```bash
+    pip install -e .
+    ```
+
 ### MuJoCo
 Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py)
 
@@ -62,6 +69,57 @@ pip install pytest
 pytest
 ```
 
+## Training models
+Most of the algorithms in baselines repo are used as follows:
+```bash
+python -m baselines.run --alg=<name of the algorithm> --env=<environment_id> [additional arguments]
+```
+### Example 1. PPO with MuJoCo Humanoid
+For instance, to train a fully-connected network controlling MuJoCo humanoid using PPO2 for 20M timesteps
+```bash
+python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7
+```
+Note that for mujoco environments fully-connected network is default, so we can omit `--network=mlp`
+The hyperparameters for both network and the learning algorithm can be controlled via the command line, for instance:
+```bash
+python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy
+```
+will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same)
+
+See docstrings in [common/models.py](baselines/common/models.py) for description of network parameters for each type of model, and 
+docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparamters. 
+
+### Example 2. DQN on Atari 
+DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong:
+```
+python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 --num_timesteps=1e6
+```
+
+## Saving, loading and visualizing models
+The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models. 
+`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively. 
+Let's imagine you'd like to train ppo2 on Atari Pong,  save the model and then later visualize what has it learnt.
+```bash
+python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2
+```
+This should get to the mean reward per episode about 20. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize: 
+```bash
+python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
+```
+
+*NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default
+
+
+## Using baselines with TensorBoard
+Baselines logger can save data in the TensorBoard format. To do so, set environment variables `OPENAI_LOG_FORMAT` and `OPENAI_LOGDIR`:
+```bash
+export OPENAI_LOG_FORMAT='stdout,log,csv,tensorboard' # formats are comma-separated, but for tensorboard you only really need the last one
+export OPENAI_LOGDIR=path/to/tensorboard/data
+```
+And you can now start TensorBoard with:
+```bash
+tensorboard --logdir=$OPENAI_LOGDIR
+```
 ## Subpackages
 
 - [A2C](baselines/a2c)
@@ -71,17 +129,27 @@ pytest
 - [DQN](baselines/deepq)
 - [GAIL](baselines/gail)
 - [HER](baselines/her)
-- [PPO1](baselines/ppo1) (Multi-CPU using MPI)
-- [PPO2](baselines/ppo2) (Optimized for GPU)
+- [PPO1](baselines/ppo1) (obsolete version, left here temporarily)
+- [PPO2](baselines/ppo2) 
 - [TRPO](baselines/trpo_mpi)
 
+
+
+## Benchmarks
+Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available 
+[here for Mujoco](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm) 
+and
+[here for Atari](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_atari10M.htm) 
+respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page. 
+
 To cite this repository in publications:
 
     @misc{baselines,
-      author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai},
+      author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai and Zhokhov, Peter},
       title = {OpenAI Baselines},
       year = {2017},
       publisher = {GitHub},
       journal = {GitHub repository},
       howpublished = {\url{https://github.com/openai/baselines}},
     }
+

baselines/__init__.py

@@ -0,0 +1,12 @@
+# explicitly import sub-packages to register algorithms
+
+import baselines.a2c.a2c
+import baselines.acer.acer
+import baselines.acktr.acktr
+import baselines.deepq.deepq
+import baselines.ddpg.ddpg
+import baselines.ppo2.ppo2
+
+# not really sure why flake8 complains only about trpo_mpi here...
+import baselines.trpo_mpi.trpo_mpi # noqa: F401
+

baselines/a2c/README.md

@@ -2,4 +2,12 @@
 
 - Original paper: https://arxiv.org/abs/1602.01783
 - Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
-- `python -m baselines.a2c.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
+- `python -m baselines.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options
+- also refer to the repo-wide [README.md](../../README.md#training-models)
+
+## Files
+- `run_atari`: file used to run the algorithm.
+- `policies.py`: contains the different versions of the A2C architecture (MlpPolicy, CNNPolicy, LstmPolicy...).
+- `a2c.py`: - Model : class used to initialize the step_model (sampling) and train_model (training)
+	- learn : Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
+- `runner.py`: class used to generates a batch of experiences

baselines/a2c/a2c.py

@@ -1,55 +1,96 @@
-import os.path as osp
 import time
-import joblib
-import numpy as np
+import functools
 import tensorflow as tf
-from baselines import logger
+
+from baselines import logger, registry
 
 from baselines.common import set_global_seeds, explained_variance
-from baselines.common.runners import AbstractEnvRunner
 from baselines.common import tf_util
+from baselines.common.policies import build_policy
 
-from baselines.a2c.utils import discount_with_dones
-from baselines.a2c.utils import Scheduler, make_path, find_trainable_variables
-from baselines.a2c.utils import cat_entropy, mse
+from baselines.a2c.utils import Scheduler, find_trainable_variables
+from baselines.a2c.runner import Runner
+
+from tensorflow import losses
 
 class Model(object):
 
-    def __init__(self, policy, ob_space, ac_space, nenvs, nsteps,
+    """
+    We use this class to :
+        __init__:
+        - Creates the step_model
+        - Creates the train_model
+
+        train():
+        - Make the training part (feedforward and retropropagation of gradients)
+
+        save/load():
+        - Save load the model
+    """
+    def __init__(self, policy, env, nsteps,
             ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,
             alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6), lrschedule='linear'):
 
-        sess = tf_util.make_session()
+        sess = tf_util.get_session()
+        nenvs = env.num_envs
         nbatch = nenvs*nsteps
 
-        A = tf.placeholder(tf.int32, [nbatch])
+
+        with tf.variable_scope('a2c_model', reuse=tf.AUTO_REUSE):
+            # step_model is used for sampling
+            step_model = policy(nenvs, 1, sess)
+
+            # train_model is used to train our network
+            train_model = policy(nbatch, nsteps, sess)
+
+        A = tf.placeholder(train_model.action.dtype, train_model.action.shape)
         ADV = tf.placeholder(tf.float32, [nbatch])
         R = tf.placeholder(tf.float32, [nbatch])
         LR = tf.placeholder(tf.float32, [])
 
-        step_model = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False)
-        train_model = policy(sess, ob_space, ac_space, nenvs*nsteps, nsteps, reuse=True)
+        # Calculate the loss
+        # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
 
-        neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)
+        # Policy loss
+        neglogpac = train_model.pd.neglogp(A)
+        # L = A(s,a) * -logpi(a|s)
         pg_loss = tf.reduce_mean(ADV * neglogpac)
-        vf_loss = tf.reduce_mean(mse(tf.squeeze(train_model.vf), R))
-        entropy = tf.reduce_mean(cat_entropy(train_model.pi))
+
+        # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
+        entropy = tf.reduce_mean(train_model.pd.entropy())
+
+        # Value loss
+        vf_loss = losses.mean_squared_error(tf.squeeze(train_model.vf), R)
+
         loss = pg_loss - entropy*ent_coef + vf_loss * vf_coef
 
-        params = find_trainable_variables("model")
+        # Update parameters using loss
+        # 1. Get the model parameters
+        params = find_trainable_variables("a2c_model")
+
+        # 2. Calculate the gradients
         grads = tf.gradients(loss, params)
         if max_grad_norm is not None:
+            # Clip the gradients (normalize)
             grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
         grads = list(zip(grads, params))
+        # zip aggregate each gradient with parameters associated
+        # For instance zip(ABCD, xyza) => Ax, By, Cz, Da
+
+        # 3. Make op for one policy and value update step of A2C
         trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon)
+
         _train = trainer.apply_gradients(grads)
 
         lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)
 
         def train(obs, states, rewards, masks, actions, values):
+            # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
+            # rewards = R + yV(s')
             advs = rewards - values
             for step in range(len(obs)):
                 cur_lr = lr.value()
+
             td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, LR:cur_lr}
             if states is not None:
                 td_map[train_model.S] = states
@@ -60,17 +101,6 @@ class Model(object):
             )
             return policy_loss, value_loss, policy_entropy
 
-        def save(save_path):
-            ps = sess.run(params)
-            make_path(osp.dirname(save_path))
-            joblib.dump(ps, save_path)
-
-        def load(load_path):
-            loaded_params = joblib.load(load_path)
-            restores = []
-            for p, loaded_p in zip(params, loaded_params):
-                restores.append(p.assign(loaded_p))
-            sess.run(restores)
 
         self.train = train
         self.train_model = train_model
@@ -78,76 +108,112 @@ class Model(object):
         self.step = step_model.step
         self.value = step_model.value
         self.initial_state = step_model.initial_state
-        self.save = save
-        self.load = load
+        self.save = functools.partial(tf_util.save_variables, sess=sess)
+        self.load = functools.partial(tf_util.load_variables, sess=sess)
         tf.global_variables_initializer().run(session=sess)
 
-class Runner(AbstractEnvRunner):
 
-    def __init__(self, env, model, nsteps=5, gamma=0.99):
-        super().__init__(env=env, model=model, nsteps=nsteps)
-        self.gamma = gamma
+@registry.register('a2c')
+def learn(
+    network,
+    env,
+    seed=None,
+    nsteps=5,
+    total_timesteps=int(80e6),
+    vf_coef=0.5,
+    ent_coef=0.01,
+    max_grad_norm=0.5,
+    lr=7e-4,
+    lrschedule='linear',
+    epsilon=1e-5,
+    alpha=0.99,
+    gamma=0.99,
+    log_interval=100,
+    load_path=None,
+    **network_kwargs):
+
+    '''
+    Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
+
+    Parameters:
+    -----------
+
+    network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
+                        specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
+                        tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
+                        neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
+                        See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
+
+
+    env:                RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
+
+
+    seed:               seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
+
+    nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
+                        nenv is number of environment copies simulated in parallel)
+
+    total_timesteps:    int, total number of timesteps to train on (default: 80M)
+
+    vf_coef:            float, coefficient in front of value function loss in the total loss function (default: 0.5)
+
+    ent_coef:           float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
+
+    max_gradient_norm:  float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
+
+    lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
+
+    lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
+                        returns fraction of the learning rate (specified as lr) as output
+
+    epsilon:            float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
+
+    alpha:              float, RMSProp decay parameter (default: 0.99)
+
+    gamma:              float, reward discounting parameter (default: 0.99)
+
+    log_interval:       int, specifies how frequently the logs are printed out (default: 100)
+
+    **network_kwargs:   keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+                        For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
+
+    '''
+
 
-    def run(self):
-        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[]
-        mb_states = self.states
-        for n in range(self.nsteps):
-            actions, values, states, _ = self.model.step(self.obs, self.states, self.dones)
-            mb_obs.append(np.copy(self.obs))
-            mb_actions.append(actions)
-            mb_values.append(values)
-            mb_dones.append(self.dones)
-            obs, rewards, dones, _ = self.env.step(actions)
-            self.states = states
-            self.dones = dones
-            for n, done in enumerate(dones):
-                if done:
-                    self.obs[n] = self.obs[n]*0
-            self.obs = obs
-            mb_rewards.append(rewards)
-        mb_dones.append(self.dones)
-        #batch of steps to batch of rollouts
-        mb_obs = np.asarray(mb_obs, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape)
-        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
-        mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)
-        mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
-        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
-        mb_masks = mb_dones[:, :-1]
-        mb_dones = mb_dones[:, 1:]
-        last_values = self.model.value(self.obs, self.states, self.dones).tolist()
-        #discount/bootstrap off value fn
-        for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
-            rewards = rewards.tolist()
-            dones = dones.tolist()
-            if dones[-1] == 0:
-                rewards = discount_with_dones(rewards+[value], dones+[0], self.gamma)[:-1]
-            else:
-                rewards = discount_with_dones(rewards, dones, self.gamma)
-            mb_rewards[n] = rewards
-        mb_rewards = mb_rewards.flatten()
-        mb_actions = mb_actions.flatten()
-        mb_values = mb_values.flatten()
-        mb_masks = mb_masks.flatten()
-        return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values
 
-def learn(policy, env, seed, nsteps=5, total_timesteps=int(80e6), vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, lrschedule='linear', epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=100):
     set_global_seeds(seed)
 
+    # Get the nb of env
     nenvs = env.num_envs
-    ob_space = env.observation_space
-    ac_space = env.action_space
-    model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
+    policy = build_policy(env, network, **network_kwargs)
+
+    # Instantiate the model object (that creates step_model and train_model)
+    model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
         max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
+    if load_path is not None:
+        model.load(load_path)
+
+    # Instantiate the runner object
     runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
 
+    # Calculate the batch_size
     nbatch = nenvs*nsteps
+
+    # Start total timer
     tstart = time.time()
+
     for update in range(1, total_timesteps//nbatch+1):
+        # Get mini batch of experiences
         obs, states, rewards, masks, actions, values = runner.run()
+
         policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
         nseconds = time.time()-tstart
+
+        # Calculate the fps (frame per second)
         fps = int((update*nbatch)/nseconds)
         if update % log_interval == 0 or update == 1:
+            # Calculates if value function is a good predicator of the returns (ev > 1)
+            # or if it's just worse than predicting nothing (ev =< 0)
             ev = explained_variance(values, rewards)
             logger.record_tabular("nupdates", update)
             logger.record_tabular("total_timesteps", update*nbatch)
@@ -156,5 +222,5 @@ def learn(policy, env, seed, nsteps=5, total_timesteps=int(80e6), vf_coef=0.5, e
             logger.record_tabular("value_loss", float(value_loss))
             logger.record_tabular("explained_variance", float(ev))
             logger.dump_tabular()
-    env.close()
     return model
+

baselines/a2c/policies.py

@@ -1,146 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch, lstm, lnlstm
-from baselines.common.distributions import make_pdtype
-from baselines.common.input import observation_input
-
-def nature_cnn(unscaled_images, **conv_kwargs):
-    """
-    CNN from Nature paper.
-    """
-    scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
-    activ = tf.nn.relu
-    h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
-                   **conv_kwargs))
-    h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
-    h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
-    h3 = conv_to_fc(h3)
-    return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
-
-class LnLstmPolicy(object):
-    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, nlstm=256, reuse=False):
-        nenv = nbatch // nsteps
-        X, processed_x = observation_input(ob_space, nbatch)
-        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
-        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
-        self.pdtype = make_pdtype(ac_space)
-        with tf.variable_scope("model", reuse=reuse):
-            h = nature_cnn(processed_x)
-            xs = batch_to_seq(h, nenv, nsteps)
-            ms = batch_to_seq(M, nenv, nsteps)
-            h5, snew = lnlstm(xs, ms, S, 'lstm1', nh=nlstm)
-            h5 = seq_to_batch(h5)
-            vf = fc(h5, 'v', 1)
-            self.pd, self.pi = self.pdtype.pdfromlatent(h5)
-
-        v0 = vf[:, 0]
-        a0 = self.pd.sample()
-        neglogp0 = self.pd.neglogp(a0)
-        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)
-
-        def step(ob, state, mask):
-            return sess.run([a0, v0, snew, neglogp0], {X:ob, S:state, M:mask})
-
-        def value(ob, state, mask):
-            return sess.run(v0, {X:ob, S:state, M:mask})
-
-        self.X = X
-        self.M = M
-        self.S = S
-        self.vf = vf
-        self.step = step
-        self.value = value
-
-class LstmPolicy(object):
-
-    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, nlstm=256, reuse=False):
-        nenv = nbatch // nsteps
-        self.pdtype = make_pdtype(ac_space)
-        X, processed_x = observation_input(ob_space, nbatch)
-
-        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
-        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
-        with tf.variable_scope("model", reuse=reuse):
-            h = nature_cnn(X)
-            xs = batch_to_seq(h, nenv, nsteps)
-            ms = batch_to_seq(M, nenv, nsteps)
-            h5, snew = lstm(xs, ms, S, 'lstm1', nh=nlstm)
-            h5 = seq_to_batch(h5)
-            vf = fc(h5, 'v', 1)
-            self.pd, self.pi = self.pdtype.pdfromlatent(h5)
-
-        v0 = vf[:, 0]
-        a0 = self.pd.sample()
-        neglogp0 = self.pd.neglogp(a0)
-        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)
-
-        def step(ob, state, mask):
-            return sess.run([a0, v0, snew, neglogp0], {X:ob, S:state, M:mask})
-
-        def value(ob, state, mask):
-            return sess.run(v0, {X:ob, S:state, M:mask})
-
-        self.X = X
-        self.M = M
-        self.S = S
-        self.vf = vf
-        self.step = step
-        self.value = value
-
-class CnnPolicy(object):
-
-    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False, **conv_kwargs): #pylint: disable=W0613
-        self.pdtype = make_pdtype(ac_space)
-        X, processed_x = observation_input(ob_space, nbatch)
-        with tf.variable_scope("model", reuse=reuse):
-            h = nature_cnn(processed_x, **conv_kwargs)
-            vf = fc(h, 'v', 1)[:,0]
-            self.pd, self.pi = self.pdtype.pdfromlatent(h, init_scale=0.01)
-
-        a0 = self.pd.sample()
-        neglogp0 = self.pd.neglogp(a0)
-        self.initial_state = None
-
-        def step(ob, *_args, **_kwargs):
-            a, v, neglogp = sess.run([a0, vf, neglogp0], {X:ob})
-            return a, v, self.initial_state, neglogp
-
-        def value(ob, *_args, **_kwargs):
-            return sess.run(vf, {X:ob})
-
-        self.X = X
-        self.vf = vf
-        self.step = step
-        self.value = value
-
-class MlpPolicy(object):
-    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False): #pylint: disable=W0613
-        self.pdtype = make_pdtype(ac_space)
-        with tf.variable_scope("model", reuse=reuse):
-            X, processed_x = observation_input(ob_space, nbatch)
-            activ = tf.tanh
-            processed_x = tf.layers.flatten(processed_x)
-            pi_h1 = activ(fc(processed_x, 'pi_fc1', nh=64, init_scale=np.sqrt(2)))
-            pi_h2 = activ(fc(pi_h1, 'pi_fc2', nh=64, init_scale=np.sqrt(2)))
-            vf_h1 = activ(fc(processed_x, 'vf_fc1', nh=64, init_scale=np.sqrt(2)))
-            vf_h2 = activ(fc(vf_h1, 'vf_fc2', nh=64, init_scale=np.sqrt(2)))
-            vf = fc(vf_h2, 'vf', 1)[:,0]
-
-            self.pd, self.pi = self.pdtype.pdfromlatent(pi_h2, init_scale=0.01)
-
-
-        a0 = self.pd.sample()
-        neglogp0 = self.pd.neglogp(a0)
-        self.initial_state = None
-
-        def step(ob, *_args, **_kwargs):
-            a, v, neglogp = sess.run([a0, vf, neglogp0], {X:ob})
-            return a, v, self.initial_state, neglogp
-
-        def value(ob, *_args, **_kwargs):
-            return sess.run(vf, {X:ob})
-
-        self.X = X
-        self.vf = vf
-        self.step = step
-        self.value = value

baselines/a2c/run_atari.py

@@ -1,30 +0,0 @@
-#!/usr/bin/env python3
-
-from baselines import logger
-from baselines.common.cmd_util import make_atari_env, atari_arg_parser
-from baselines.common.vec_env.vec_frame_stack import VecFrameStack
-from baselines.a2c.a2c import learn
-from baselines.ppo2.policies import CnnPolicy, LstmPolicy, LnLstmPolicy
-
-def train(env_id, num_timesteps, seed, policy, lrschedule, num_env):
-    if policy == 'cnn':
-        policy_fn = CnnPolicy
-    elif policy == 'lstm':
-        policy_fn = LstmPolicy
-    elif policy == 'lnlstm':
-        policy_fn = LnLstmPolicy
-    env = VecFrameStack(make_atari_env(env_id, num_env, seed), 4)
-    learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), lrschedule=lrschedule)
-    env.close()
-
-def main():
-    parser = atari_arg_parser()
-    parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm'], default='cnn')
-    parser.add_argument('--lrschedule', help='Learning rate schedule', choices=['constant', 'linear'], default='constant')
-    args = parser.parse_args()
-    logger.configure()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
-        policy=args.policy, lrschedule=args.lrschedule, num_env=16)
-
-if __name__ == '__main__':
-    main()

baselines/a2c/runner.py

@@ -0,0 +1,75 @@
+import numpy as np
+from baselines.a2c.utils import discount_with_dones
+from baselines.common.runners import AbstractEnvRunner
+
+class Runner(AbstractEnvRunner):
+    """
+    We use this class to generate batches of experiences
+
+    __init__:
+    - Initialize the runner
+
+    run():
+    - Make a mini batch of experiences
+    """
+    def __init__(self, env, model, nsteps=5, gamma=0.99):
+        super().__init__(env=env, model=model, nsteps=nsteps)
+        self.gamma = gamma
+        self.batch_action_shape = [x if x is not None else -1 for x in model.train_model.action.shape.as_list()]
+        self.ob_dtype = model.train_model.X.dtype.as_numpy_dtype
+
+    def run(self):
+        # We initialize the lists that will contain the mb of experiences
+        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[]
+        mb_states = self.states
+        for n in range(self.nsteps):
+            # Given observations, take action and value (V(s))
+            # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
+            actions, values, states, _ = self.model.step(self.obs, S=self.states, M=self.dones)
+
+            # Append the experiences
+            mb_obs.append(np.copy(self.obs))
+            mb_actions.append(actions)
+            mb_values.append(values)
+            mb_dones.append(self.dones)
+
+            # Take actions in env and look the results
+            obs, rewards, dones, _ = self.env.step(actions)
+            self.states = states
+            self.dones = dones
+            for n, done in enumerate(dones):
+                if done:
+                    self.obs[n] = self.obs[n]*0
+            self.obs = obs
+            mb_rewards.append(rewards)
+        mb_dones.append(self.dones)
+
+        # Batch of steps to batch of rollouts
+        mb_obs = np.asarray(mb_obs, dtype=self.ob_dtype).swapaxes(1, 0).reshape(self.batch_ob_shape)
+        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
+        mb_actions = np.asarray(mb_actions, dtype=self.model.train_model.action.dtype.name).swapaxes(1, 0)
+        mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
+        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
+        mb_masks = mb_dones[:, :-1]
+        mb_dones = mb_dones[:, 1:]
+
+
+        if self.gamma > 0.0:
+            # Discount/bootstrap off value fn
+            last_values = self.model.value(self.obs, S=self.states, M=self.dones).tolist()
+            for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
+                rewards = rewards.tolist()
+                dones = dones.tolist()
+                if dones[-1] == 0:
+                    rewards = discount_with_dones(rewards+[value], dones+[0], self.gamma)[:-1]
+                else:
+                    rewards = discount_with_dones(rewards, dones, self.gamma)
+
+                mb_rewards[n] = rewards
+
+        mb_actions = mb_actions.reshape(self.batch_action_shape)
+
+        mb_rewards = mb_rewards.flatten()
+        mb_values = mb_values.flatten()
+        mb_masks = mb_masks.flatten()
+        return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values

baselines/a2c/utils.py

@@ -1,8 +1,6 @@
 import os
-import gym
 import numpy as np
 import tensorflow as tf
-from gym import spaces
 from collections import deque
 
 def sample(logits):
@@ -10,18 +8,15 @@ def sample(logits):
     return tf.argmax(logits - tf.log(-tf.log(noise)), 1)
 
 def cat_entropy(logits):
-    a0 = logits - tf.reduce_max(logits, 1, keep_dims=True)
+    a0 = logits - tf.reduce_max(logits, 1, keepdims=True)
     ea0 = tf.exp(a0)
-    z0 = tf.reduce_sum(ea0, 1, keep_dims=True)
+    z0 = tf.reduce_sum(ea0, 1, keepdims=True)
     p0 = ea0 / z0
     return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1)
 
 def cat_entropy_softmax(p0):
     return - tf.reduce_sum(p0 * tf.log(p0 + 1e-6), axis = 1)
 
-def mse(pred, target):
-    return tf.square(pred-target)/2.
-
 def ortho_init(scale=1.0):
     def _ortho_init(shape, dtype, partition_info=None):
         #lasagne ortho init for tf
@@ -58,7 +53,7 @@ def conv(x, scope, *, nf, rf, stride, pad='VALID', init_scale=1.0, data_format='
         b = tf.get_variable("b", bias_var_shape, initializer=tf.constant_initializer(0.0))
         if not one_dim_bias and data_format == 'NHWC':
             b = tf.reshape(b, bshape)
-        return b + tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format)
+        return tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format) + b
 
 def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0):
     with tf.variable_scope(scope):
@@ -85,7 +80,6 @@ def seq_to_batch(h, flat = False):
 
 def lstm(xs, ms, s, scope, nh, init_scale=1.0):
     nbatch, nin = [v.value for v in xs[0].get_shape()]
-    nsteps = len(xs)
     with tf.variable_scope(scope):
         wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
         wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
@@ -115,7 +109,6 @@ def _ln(x, g, b, e=1e-5, axes=[1]):
 
 def lnlstm(xs, ms, s, scope, nh, init_scale=1.0):
     nbatch, nin = [v.value for v in xs[0].get_shape()]
-    nsteps = len(xs)
     with tf.variable_scope(scope):
         wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
         gx = tf.get_variable("gx", [nh*4], initializer=tf.constant_initializer(1.0))
@@ -160,8 +153,7 @@ def discount_with_dones(rewards, dones, gamma):
     return discounted[::-1]
 
 def find_trainable_variables(key):
-    with tf.variable_scope(key):
-        return tf.trainable_variables()
+    return tf.trainable_variables(key)
 
 def make_path(f):
     return os.makedirs(f, exist_ok=True)

baselines/acer/README.md

@@ -1,4 +1,6 @@
 # ACER
 
 - Original paper: https://arxiv.org/abs/1611.01224
-- `python -m baselines.acer.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
+- `python -m baselines.run --alg=acer --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- also refer to the repo-wide [README.md](../../README.md#training-models)
+

baselines/acer/acer.py

@@ -1,20 +1,22 @@
 import time
-import joblib
+import functools
 import numpy as np
 import tensorflow as tf
-from baselines import logger
+from baselines import logger, registry
 
 from baselines.common import set_global_seeds
-from baselines.common.runners import AbstractEnvRunner
+from baselines.common.policies import build_policy
+from baselines.common.tf_util import get_session, save_variables
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 
 from baselines.a2c.utils import batch_to_seq, seq_to_batch
-from baselines.a2c.utils import Scheduler, make_path, find_trainable_variables
 from baselines.a2c.utils import cat_entropy_softmax
+from baselines.a2c.utils import Scheduler, find_trainable_variables
 from baselines.a2c.utils import EpisodeStats
 from baselines.a2c.utils import get_by_index, check_shape, avg_norm, gradient_add, q_explained_variance
 from baselines.acer.buffer import Buffer
-
-import os.path as osp
+from baselines.acer.runner import Runner
+from baselines.acer.defaults import defaults
 
 # remove last step
 def strip(var, nenvs, nsteps, flat = False):
@@ -55,14 +57,11 @@ def q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma):
 #     return tf.minimum(1 + eps_clip, tf.maximum(1 - eps_clip, ratio))
 
 class Model(object):
-    def __init__(self, policy, ob_space, ac_space, nenvs, nsteps, nstack, num_procs,
-                 ent_coef, q_coef, gamma, max_grad_norm, lr,
+    def __init__(self, policy, ob_space, ac_space, nenvs, nsteps, ent_coef, q_coef, gamma, max_grad_norm, lr,
                  rprop_alpha, rprop_epsilon, total_timesteps, lrschedule,
                  c, trust_region, alpha, delta):
-        config = tf.ConfigProto(allow_soft_placement=True,
-                                intra_op_parallelism_threads=num_procs,
-                                inter_op_parallelism_threads=num_procs)
-        sess = tf.Session(config=config)
+
+        sess = get_session()
         nact = ac_space.n
         nbatch = nenvs * nsteps
 
@@ -73,10 +72,15 @@ class Model(object):
         LR = tf.placeholder(tf.float32, [])
         eps = 1e-6
 
-        step_model = policy(sess, ob_space, ac_space, nenvs, 1, nstack, reuse=False)
-        train_model = policy(sess, ob_space, ac_space, nenvs, nsteps + 1, nstack, reuse=True)
+        step_ob_placeholder = tf.placeholder(dtype=ob_space.dtype, shape=(nenvs,) + ob_space.shape)
+        train_ob_placeholder = tf.placeholder(dtype=ob_space.dtype, shape=(nenvs*(nsteps+1),) + ob_space.shape)
+        with tf.variable_scope('acer_model', reuse=tf.AUTO_REUSE):
 
-        params = find_trainable_variables("model")
+            step_model = policy(observ_placeholder=step_ob_placeholder, sess=sess)
+            train_model = policy(observ_placeholder=train_ob_placeholder, sess=sess)
+
+
+        params = find_trainable_variables("acer_model")
         print("Params {}".format(len(params)))
         for var in params:
             print(var)
@@ -90,14 +94,20 @@ class Model(object):
             print(v.name)
             return v
 
-        with tf.variable_scope("", custom_getter=custom_getter, reuse=True):
-            polyak_model = policy(sess, ob_space, ac_space, nenvs, nsteps + 1, nstack, reuse=True)
+        with tf.variable_scope("acer_model", custom_getter=custom_getter, reuse=True):
+            polyak_model = policy(observ_placeholder=train_ob_placeholder, sess=sess)
 
         # Notation: (var) = batch variable, (var)s = seqeuence variable, (var)_i = variable index by action at step i
-        v = tf.reduce_sum(train_model.pi * train_model.q, axis = -1) # shape is [nenvs * (nsteps + 1)]
+
+        # action probability distributions according to train_model, polyak_model and step_model
+        # poilcy.pi is probability distribution parameters; to obtain distribution that sums to 1 need to take softmax
+        train_model_p = tf.nn.softmax(train_model.pi)
+        polyak_model_p = tf.nn.softmax(polyak_model.pi)
+        step_model_p = tf.nn.softmax(step_model.pi)
+        v = tf.reduce_sum(train_model_p * train_model.q, axis = -1) # shape is [nenvs * (nsteps + 1)]
 
         # strip off last step
-        f, f_pol, q = map(lambda var: strip(var, nenvs, nsteps), [train_model.pi, polyak_model.pi, train_model.q])
+        f, f_pol, q = map(lambda var: strip(var, nenvs, nsteps), [train_model_p, polyak_model_p, train_model.q])
         # Get pi and q values for actions taken
         f_i = get_by_index(f, A)
         q_i = get_by_index(q, A)
@@ -111,6 +121,7 @@ class Model(object):
 
         # Calculate losses
         # Entropy
+        # entropy = tf.reduce_mean(strip(train_model.pd.entropy(), nenvs, nsteps))
         entropy = tf.reduce_mean(cat_entropy_softmax(f))
 
         # Policy Graident loss, with truncated importance sampling & bias correction
@@ -192,80 +203,29 @@ class Model(object):
         def train(obs, actions, rewards, dones, mus, states, masks, steps):
             cur_lr = lr.value_steps(steps)
             td_map = {train_model.X: obs, polyak_model.X: obs, A: actions, R: rewards, D: dones, MU: mus, LR: cur_lr}
-            if states != []:
+            if states is not None:
                 td_map[train_model.S] = states
                 td_map[train_model.M] = masks
                 td_map[polyak_model.S] = states
                 td_map[polyak_model.M] = masks
+
             return names_ops, sess.run(run_ops, td_map)[1:]  # strip off _train
 
-        def save(save_path):
-            ps = sess.run(params)
-            make_path(osp.dirname(save_path))
-            joblib.dump(ps, save_path)
+        def _step(observation, **kwargs):
+            return step_model._evaluate([step_model.action, step_model_p, step_model.state], observation, **kwargs)
+
+
 
         self.train = train
-        self.save = save
+        self.save = functools.partial(save_variables, sess=sess, variables=params)
         self.train_model = train_model
         self.step_model = step_model
-        self.step = step_model.step
+        self._step = _step
+        self.step = self.step_model.step
+
         self.initial_state = step_model.initial_state
         tf.global_variables_initializer().run(session=sess)
 
-class Runner(AbstractEnvRunner):
-    def __init__(self, env, model, nsteps, nstack):
-        super().__init__(env=env, model=model, nsteps=nsteps)
-        self.nstack = nstack
-        nh, nw, nc = env.observation_space.shape
-        self.nc = nc  # nc = 1 for atari, but just in case
-        self.nenv = nenv = env.num_envs
-        self.nact = env.action_space.n
-        self.nbatch = nenv * nsteps
-        self.batch_ob_shape = (nenv*(nsteps+1), nh, nw, nc*nstack)
-        self.obs = np.zeros((nenv, nh, nw, nc * nstack), dtype=np.uint8)
-        obs = env.reset()
-        self.update_obs(obs)
-
-    def update_obs(self, obs, dones=None):
-        if dones is not None:
-            self.obs *= (1 - dones.astype(np.uint8))[:, None, None, None]
-        self.obs = np.roll(self.obs, shift=-self.nc, axis=3)
-        self.obs[:, :, :, -self.nc:] = obs[:, :, :, :]
-
-    def run(self):
-        enc_obs = np.split(self.obs, self.nstack, axis=3)  # so now list of obs steps
-        mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], []
-        for _ in range(self.nsteps):
-            actions, mus, states = self.model.step(self.obs, state=self.states, mask=self.dones)
-            mb_obs.append(np.copy(self.obs))
-            mb_actions.append(actions)
-            mb_mus.append(mus)
-            mb_dones.append(self.dones)
-            obs, rewards, dones, _ = self.env.step(actions)
-            # states information for statefull models like LSTM
-            self.states = states
-            self.dones = dones
-            self.update_obs(obs, dones)
-            mb_rewards.append(rewards)
-            enc_obs.append(obs)
-        mb_obs.append(np.copy(self.obs))
-        mb_dones.append(self.dones)
-
-        enc_obs = np.asarray(enc_obs, dtype=np.uint8).swapaxes(1, 0)
-        mb_obs = np.asarray(mb_obs, dtype=np.uint8).swapaxes(1, 0)
-        mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)
-        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
-        mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0)
-
-        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
-
-        mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done
-        mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards
-
-        # shapes are now [nenv, nsteps, []]
-        # When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy.
-
-        return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks
 
 class Acer():
     def __init__(self, runner, model, buffer, log_interval):
@@ -288,6 +248,7 @@ class Acer():
             # get obs, actions, rewards, mus, dones from buffer.
             obs, actions, rewards, mus, dones, masks = buffer.get()
 
+
         # reshape stuff correctly
         obs = obs.reshape(runner.batch_ob_shape)
         actions = actions.reshape([runner.nbatch])
@@ -310,34 +271,103 @@ class Acer():
                 logger.record_tabular(name, float(val))
             logger.dump_tabular()
 
-
-def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
+@registry.register('acer', defaults=defaults)
+def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
           max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
           log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
-          trust_region=True, alpha=0.99, delta=1):
+          trust_region=True, alpha=0.99, delta=1, load_path=None, **network_kwargs):
+
+    '''
+    Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
+    Train an agent with given network architecture on a given environment using ACER.
+
+    Parameters:
+    ----------
+
+    network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
+                        specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
+                        tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
+                        neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
+                        See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
+
+    env:                environment. Needs to be vectorized for parallel environment simulation.
+                        The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
+
+    nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
+                        nenv is number of environment copies simulated in parallel) (default: 20)
+
+    nstack:             int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
+                        (last image dimension) (default: 4)
+
+    total_timesteps:    int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
+
+    q_coef:             float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
+
+    ent_coef:           float, policy entropy coefficient in the optimization objective (default: 0.01)
+
+    max_grad_norm:      float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
+
+    lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
+
+    lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
+                        returns fraction of the learning rate (specified as lr) as output
+
+    rprop_epsilon:      float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
+
+    rprop_alpha:        float, RMSProp decay parameter (default: 0.99)
+
+    gamma:              float, reward discounting factor (default: 0.99)
+
+    log_interval:       int, number of updates between logging events (default: 100)
+
+    buffer_size:        int, size of the replay buffer (default: 50k)
+
+    replay_ratio:       int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
+
+    replay_start:       int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
+
+    c:                  float, importance weight clipping factor (default: 10)
+
+    trust_region        bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size  (default: True)
+
+    delta:              float, max KL divergence between the old policy and updated policy (default: 1)
+
+    alpha:              float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
+
+    load_path:          str, path to load the model from (default: None)
+
+    **network_kwargs:               keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+                                    For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
+
+    '''
+
     print("Running Acer Simple")
     print(locals())
-    tf.reset_default_graph()
     set_global_seeds(seed)
+    if not isinstance(env, VecFrameStack):
+        env = VecFrameStack(env, 1)
 
+    policy = build_policy(env, network, estimate_q=True, **network_kwargs)
     nenvs = env.num_envs
     ob_space = env.observation_space
     ac_space = env.action_space
-    num_procs = len(env.remotes) # HACK
-    model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, nstack=nstack,
-                  num_procs=num_procs, ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
+
+    nstack = env.nstack
+    model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps,
+                  ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
                   max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
                   total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
                   trust_region=trust_region, alpha=alpha, delta=delta)
 
-    runner = Runner(env=env, model=model, nsteps=nsteps, nstack=nstack)
+    runner = Runner(env=env, model=model, nsteps=nsteps)
     if replay_ratio > 0:
-        buffer = Buffer(env=env, nsteps=nsteps, nstack=nstack, size=buffer_size)
+        buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
     else:
         buffer = None
     nbatch = nenvs*nsteps
     acer = Acer(runner, model, buffer, log_interval)
     acer.tstart = time.time()
+
     for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
         acer.call(on_policy=True)
         if replay_ratio > 0 and buffer.has_atleast(replay_start):
@@ -345,4 +375,4 @@ def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_c
             for _ in range(n):
                 acer.call(on_policy=False)  # no simulation steps in this
 
-    env.close()
+    return model

baselines/acer/buffer.py

@@ -2,11 +2,16 @@ import numpy as np
 
 class Buffer(object):
     # gets obs, actions, rewards, mu's, (states, masks), dones
-    def __init__(self, env, nsteps, nstack, size=50000):
+    def __init__(self, env, nsteps, size=50000):
         self.nenv = env.num_envs
         self.nsteps = nsteps
-        self.nh, self.nw, self.nc = env.observation_space.shape
-        self.nstack = nstack
+        # self.nh, self.nw, self.nc = env.observation_space.shape
+        self.obs_shape = env.observation_space.shape
+        self.obs_dtype = env.observation_space.dtype
+        self.ac_dtype = env.action_space.dtype
+        self.nc = self.obs_shape[-1]
+        self.nstack = env.nstack
+        self.nc //= self.nstack
         self.nbatch = self.nenv * self.nsteps
         self.size = size // (self.nsteps)  # Each loc contains nenv * nsteps frames, thus total buffer is nenv * size frames
 
@@ -33,22 +38,11 @@ class Buffer(object):
     # Generate stacked frames
     def decode(self, enc_obs, dones):
         # enc_obs has shape [nenvs, nsteps + nstack, nh, nw, nc]
-        # dones has shape [nenvs, nsteps, nh, nw, nc]
+        # dones has shape [nenvs, nsteps]
         # returns stacked obs of shape [nenv, (nsteps + 1), nh, nw, nstack*nc]
-        nstack, nenv, nsteps, nh, nw, nc = self.nstack, self.nenv, self.nsteps, self.nh, self.nw, self.nc
-        y = np.empty([nsteps + nstack - 1, nenv, 1, 1, 1], dtype=np.float32)
-        obs = np.zeros([nstack, nsteps + nstack, nenv, nh, nw, nc], dtype=np.uint8)
-        x = np.reshape(enc_obs, [nenv, nsteps + nstack, nh, nw, nc]).swapaxes(1,
-                                                                              0)  # [nsteps + nstack, nenv, nh, nw, nc]
-        y[3:] = np.reshape(1.0 - dones, [nenv, nsteps, 1, 1, 1]).swapaxes(1, 0)  # keep
-        y[:3] = 1.0
-        # y = np.reshape(1 - dones, [nenvs, nsteps, 1, 1, 1])
-        for i in range(nstack):
-            obs[-(i + 1), i:] = x
-            # obs[:,i:,:,:,-(i+1),:] = x
-            x = x[:-1] * y
-            y = y[1:]
-        return np.reshape(obs[:, 3:].transpose((2, 1, 3, 4, 0, 5)), [nenv, (nsteps + 1), nh, nw, nstack * nc])
+
+        return _stack_obs(enc_obs, dones,
+                          nsteps=self.nsteps)
 
     def put(self, enc_obs, actions, rewards, mus, dones, masks):
         # enc_obs [nenv, (nsteps + nstack), nh, nw, nc]
@@ -56,8 +50,8 @@ class Buffer(object):
         # mus [nenv, nsteps, nact]
 
         if self.enc_obs is None:
-            self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=np.uint8)
-            self.actions = np.empty([self.size] + list(actions.shape), dtype=np.int32)
+            self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=self.obs_dtype)
+            self.actions = np.empty([self.size] + list(actions.shape), dtype=self.ac_dtype)
             self.rewards = np.empty([self.size] + list(rewards.shape), dtype=np.float32)
             self.mus = np.empty([self.size] + list(mus.shape), dtype=np.float32)
             self.dones = np.empty([self.size] + list(dones.shape), dtype=np.bool)
@@ -101,3 +95,62 @@ class Buffer(object):
         mus = take(self.mus)
         masks = take(self.masks)
         return obs, actions, rewards, mus, dones, masks
+
+
+
+def _stack_obs_ref(enc_obs, dones, nsteps):
+    nenv = enc_obs.shape[0]
+    nstack = enc_obs.shape[1] - nsteps
+    nh, nw, nc = enc_obs.shape[2:]
+    obs_dtype = enc_obs.dtype
+    obs_shape = (nh, nw, nc*nstack)
+
+    mask = np.empty([nsteps + nstack - 1, nenv, 1, 1, 1], dtype=np.float32)
+    obs = np.zeros([nstack, nsteps + nstack, nenv, nh, nw, nc], dtype=obs_dtype)
+    x = np.reshape(enc_obs, [nenv, nsteps + nstack, nh, nw, nc]).swapaxes(1, 0)  # [nsteps + nstack, nenv, nh, nw, nc]
+
+    mask[nstack-1:] = np.reshape(1.0 - dones, [nenv, nsteps, 1, 1, 1]).swapaxes(1, 0)  # keep
+    mask[:nstack-1] = 1.0
+
+    # y = np.reshape(1 - dones, [nenvs, nsteps, 1, 1, 1])
+    for i in range(nstack):
+        obs[-(i + 1), i:] = x
+        # obs[:,i:,:,:,-(i+1),:] = x
+        x = x[:-1] * mask
+        mask = mask[1:]
+
+    return np.reshape(obs[:, (nstack-1):].transpose((2, 1, 3, 4, 0, 5)), (nenv, (nsteps + 1)) + obs_shape)
+
+def _stack_obs(enc_obs, dones, nsteps):
+    nenv = enc_obs.shape[0]
+    nstack = enc_obs.shape[1] - nsteps
+    nc = enc_obs.shape[-1]
+
+    obs_ = np.zeros((nenv, nsteps + 1) + enc_obs.shape[2:-1] + (enc_obs.shape[-1] * nstack, ), dtype=enc_obs.dtype)
+    mask = np.ones((nenv, nsteps+1), dtype=enc_obs.dtype)
+    mask[:, 1:] = 1.0 - dones
+    mask = mask.reshape(mask.shape + tuple(np.ones(len(enc_obs.shape)-2, dtype=np.uint8)))
+
+    for i in range(nstack-1, -1, -1):
+        obs_[..., i * nc : (i + 1) * nc] = enc_obs[:, i : i + nsteps + 1, :]
+        if i < nstack-1:
+            obs_[..., i * nc : (i + 1) * nc] *= mask
+            mask[:, 1:, ...] *= mask[:, :-1, ...]
+
+    return obs_
+
+def test_stack_obs():
+    nstack = 7
+    nenv = 1
+    nsteps = 5
+
+    obs_shape = (2, 3, nstack)
+
+    enc_obs_shape = (nenv, nsteps + nstack) + obs_shape[:-1] + (1,)
+    enc_obs = np.random.random(enc_obs_shape)
+    dones = np.random.randint(low=0, high=2, size=(nenv, nsteps))
+
+    stacked_obs_ref = _stack_obs_ref(enc_obs, dones, nsteps=nsteps)
+    stacked_obs_test = _stack_obs(enc_obs, dones, nsteps=nsteps)
+
+    np.testing.assert_allclose(stacked_obs_ref, stacked_obs_test)

baselines/acer/defaults.py

@@ -0,0 +1,3 @@
+defaults = {
+    'atari': dict(lrschedule='constant')
+}

baselines/acer/policies.py

@@ -1,6 +1,6 @@
 import numpy as np
 import tensorflow as tf
-from baselines.ppo2.policies import nature_cnn
+from baselines.common.policies import nature_cnn
 from baselines.a2c.utils import fc, batch_to_seq, seq_to_batch, lstm, sample
 
 
@@ -18,11 +18,13 @@ class AcerCnnPolicy(object):
             pi = tf.nn.softmax(pi_logits)
             q = fc(h, 'q', nact)
 
-        a = sample(pi_logits)  # could change this to use self.pi instead
+        a = sample(tf.nn.softmax(pi_logits))  # could change this to use self.pi instead
         self.initial_state = []  # not stateful
         self.X = X
         self.pi = pi  # actual policy params now
+        self.pi_logits = pi_logits
         self.q = q
+        self.vf = q
 
         def step(ob, *args, **kwargs):
             # returns actions, mus, states

baselines/acer/run_atari.py

@@ -1,30 +0,0 @@
-#!/usr/bin/env python3
-from baselines import logger
-from baselines.acer.acer_simple import learn
-from baselines.acer.policies import AcerCnnPolicy, AcerLstmPolicy
-from baselines.common.cmd_util import make_atari_env, atari_arg_parser
-
-def train(env_id, num_timesteps, seed, policy, lrschedule, num_cpu):
-    env = make_atari_env(env_id, num_cpu, seed)
-    if policy == 'cnn':
-        policy_fn = AcerCnnPolicy
-    elif policy == 'lstm':
-        policy_fn = AcerLstmPolicy
-    else:
-        print("Policy {} not implemented".format(policy))
-        return
-    learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), lrschedule=lrschedule)
-    env.close()
-
-def main():
-    parser = atari_arg_parser()
-    parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm'], default='cnn')
-    parser.add_argument('--lrschedule', help='Learning rate schedule', choices=['constant', 'linear'], default='constant')
-    parser.add_argument('--logdir', help ='Directory for logging')
-    args = parser.parse_args()
-    logger.configure(args.logdir)
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
-          policy=args.policy, lrschedule=args.lrschedule, num_cpu=16)
-
-if __name__ == '__main__':
-    main()

baselines/acer/runner.py

@@ -0,0 +1,61 @@
+import numpy as np
+from baselines.common.runners import AbstractEnvRunner
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
+from gym import spaces
+
+
+class Runner(AbstractEnvRunner):
+
+    def __init__(self, env, model, nsteps):
+        super().__init__(env=env, model=model, nsteps=nsteps)
+        assert isinstance(env.action_space, spaces.Discrete), 'This ACER implementation works only with discrete action spaces!'
+        assert isinstance(env, VecFrameStack)
+
+        self.nact = env.action_space.n
+        nenv = self.nenv
+        self.nbatch = nenv * nsteps
+        self.batch_ob_shape = (nenv*(nsteps+1),) + env.observation_space.shape
+
+        self.obs = env.reset()
+        self.obs_dtype = env.observation_space.dtype
+        self.ac_dtype = env.action_space.dtype
+        self.nstack = self.env.nstack
+        self.nc = self.batch_ob_shape[-1] // self.nstack
+
+
+    def run(self):
+        # enc_obs = np.split(self.obs, self.nstack, axis=3)  # so now list of obs steps
+        enc_obs = np.split(self.env.stackedobs, self.env.nstack, axis=-1)
+        mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], []
+        for _ in range(self.nsteps):
+            actions, mus, states = self.model._step(self.obs, S=self.states, M=self.dones)
+            mb_obs.append(np.copy(self.obs))
+            mb_actions.append(actions)
+            mb_mus.append(mus)
+            mb_dones.append(self.dones)
+            obs, rewards, dones, _ = self.env.step(actions)
+            # states information for statefull models like LSTM
+            self.states = states
+            self.dones = dones
+            self.obs = obs
+            mb_rewards.append(rewards)
+            enc_obs.append(obs[..., -self.nc:])
+        mb_obs.append(np.copy(self.obs))
+        mb_dones.append(self.dones)
+
+        enc_obs = np.asarray(enc_obs, dtype=self.obs_dtype).swapaxes(1, 0)
+        mb_obs = np.asarray(mb_obs, dtype=self.obs_dtype).swapaxes(1, 0)
+        mb_actions = np.asarray(mb_actions, dtype=self.ac_dtype).swapaxes(1, 0)
+        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
+        mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0)
+
+        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
+
+        mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done
+        mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards
+
+        # shapes are now [nenv, nsteps, []]
+        # When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy.
+
+        return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks
+

baselines/acktr/README.md

@@ -2,4 +2,8 @@
 
 - Original paper: https://arxiv.org/abs/1708.05144
 - Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
-- `python -m baselines.acktr.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
+- `python -m baselines.run --alg=acktr --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- also refer to the repo-wide [README.md](../../README.md#training-models)
+
+## ACKTR with continuous action spaces
+The code of ACKTR has been refactored to handle both discrete and continuous action spaces uniformly. In the original version, discrete and continuous action spaces were handled by different code (actkr_disc.py and acktr_cont.py) with little overlap. If interested in the original version of the acktr for continuous action spaces, use `old_acktr_cont` branch. Note that original code performs better on the mujoco tasks than the refactored version; we are still investigating why. 

baselines/acktr/acktr.py

@@ -1,67 +1,65 @@
 import os.path as osp
 import time
-import joblib
-import numpy as np
+import functools
 import tensorflow as tf
-from baselines import logger
+from baselines import logger, registry
 
 from baselines.common import set_global_seeds, explained_variance
+from baselines.common.policies import build_policy
+from baselines.common.tf_util import get_session, save_variables, load_variables
 
-from baselines.a2c.a2c import Runner
-from baselines.a2c.utils import discount_with_dones
+from baselines.a2c.runner import Runner
 from baselines.a2c.utils import Scheduler, find_trainable_variables
-from baselines.a2c.utils import cat_entropy, mse
 from baselines.acktr import kfac
+from baselines.acktr.defaults import defaults
 
 
 class Model(object):
 
     def __init__(self, policy, ob_space, ac_space, nenvs,total_timesteps, nprocs=32, nsteps=20,
                  ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5,
-                 kfac_clip=0.001, lrschedule='linear'):
-        config = tf.ConfigProto(allow_soft_placement=True,
-                                intra_op_parallelism_threads=nprocs,
-                                inter_op_parallelism_threads=nprocs)
-        config.gpu_options.allow_growth = True
-        self.sess = sess = tf.Session(config=config)
-        nact = ac_space.n
+                 kfac_clip=0.001, lrschedule='linear', is_async=True):
+
+        self.sess = sess = get_session()
         nbatch = nenvs * nsteps
-        A = tf.placeholder(tf.int32, [nbatch])
+        A = tf.placeholder(ac_space.dtype, [nbatch,] + list(ac_space.shape))
         ADV = tf.placeholder(tf.float32, [nbatch])
         R = tf.placeholder(tf.float32, [nbatch])
         PG_LR = tf.placeholder(tf.float32, [])
         VF_LR = tf.placeholder(tf.float32, [])
 
-        self.model = step_model = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False)
-        self.model2 = train_model = policy(sess, ob_space, ac_space, nenvs*nsteps, nsteps, reuse=True)
+        with tf.variable_scope('acktr_model', reuse=tf.AUTO_REUSE):
+            self.model = step_model = policy(nenvs, 1, sess=sess)
+            self.model2 = train_model = policy(nenvs*nsteps, nsteps, sess=sess)
 
-        logpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)
-        self.logits = logits = train_model.pi
+        neglogpac = train_model.pd.neglogp(A)
+        self.logits = train_model.pi
 
         ##training loss
-        pg_loss = tf.reduce_mean(ADV*logpac)
-        entropy = tf.reduce_mean(cat_entropy(train_model.pi))
+        pg_loss = tf.reduce_mean(ADV*neglogpac)
+        entropy = tf.reduce_mean(train_model.pd.entropy())
         pg_loss = pg_loss - ent_coef * entropy
-        vf_loss = tf.reduce_mean(mse(tf.squeeze(train_model.vf), R))
+        vf_loss = tf.losses.mean_squared_error(tf.squeeze(train_model.vf), R)
         train_loss = pg_loss + vf_coef * vf_loss
 
 
         ##Fisher loss construction
-        self.pg_fisher = pg_fisher_loss = -tf.reduce_mean(logpac)
+        self.pg_fisher = pg_fisher_loss = -tf.reduce_mean(neglogpac)
         sample_net = train_model.vf + tf.random_normal(tf.shape(train_model.vf))
         self.vf_fisher = vf_fisher_loss = - vf_fisher_coef*tf.reduce_mean(tf.pow(train_model.vf - tf.stop_gradient(sample_net), 2))
         self.joint_fisher = joint_fisher_loss = pg_fisher_loss + vf_fisher_loss
 
-        self.params=params = find_trainable_variables("model")
+        self.params=params = find_trainable_variables("acktr_model")
 
         self.grads_check = grads = tf.gradients(train_loss,params)
 
         with tf.device('/gpu:0'):
             self.optim = optim = kfac.KfacOptimizer(learning_rate=PG_LR, clip_kl=kfac_clip,\
                 momentum=0.9, kfac_update=1, epsilon=0.01,\
-                stats_decay=0.99, async=1, cold_iter=10, max_grad_norm=max_grad_norm)
+                stats_decay=0.99, is_async=is_async, cold_iter=10, max_grad_norm=max_grad_norm)
 
-            update_stats_op = optim.compute_and_apply_stats(joint_fisher_loss, var_list=params)
+            # update_stats_op = optim.compute_and_apply_stats(joint_fisher_loss, var_list=params)
+            optim.compute_and_apply_stats(joint_fisher_loss, var_list=params)
             train_op, q_runner = optim.apply_gradients(list(zip(grads,params)))
         self.q_runner = q_runner
         self.lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)
@@ -71,7 +69,7 @@ class Model(object):
             for step in range(len(obs)):
                 cur_lr = self.lr.value()
 
-            td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, PG_LR:cur_lr}
+            td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, PG_LR:cur_lr, VF_LR:cur_lr}
             if states is not None:
                 td_map[train_model.S] = states
                 td_map[train_model.M] = masks
@@ -82,22 +80,10 @@ class Model(object):
             )
             return policy_loss, value_loss, policy_entropy
 
-        def save(save_path):
-            ps = sess.run(params)
-            joblib.dump(ps, save_path)
-
-        def load(load_path):
-            loaded_params = joblib.load(load_path)
-            restores = []
-            for p, loaded_p in zip(params, loaded_params):
-                restores.append(p.assign(loaded_p))
-            sess.run(restores)
-
-
 
         self.train = train
-        self.save = save
-        self.load = load
+        self.save = functools.partial(save_variables, sess=sess)
+        self.load = functools.partial(load_variables, sess=sess)
         self.train_model = train_model
         self.step_model = step_model
         self.step = step_model.step
@@ -105,30 +91,43 @@ class Model(object):
         self.initial_state = step_model.initial_state
         tf.global_variables_initializer().run(session=sess)
 
-def learn(policy, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=1, nprocs=32, nsteps=20,
+@registry.register('acktr', defaults=defaults)
+def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=1, nprocs=32, nsteps=20,
                  ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5,
-                 kfac_clip=0.001, save_interval=None, lrschedule='linear'):
-    tf.reset_default_graph()
+                 kfac_clip=0.001, save_interval=None, lrschedule='linear', load_path=None, is_async=True, **network_kwargs):
     set_global_seeds(seed)
 
+
+    if network == 'cnn':
+        network_kwargs['one_dim_bias'] = True
+
+    policy = build_policy(env, network, **network_kwargs)
+
     nenvs = env.num_envs
     ob_space = env.observation_space
     ac_space = env.action_space
     make_model = lambda : Model(policy, ob_space, ac_space, nenvs, total_timesteps, nprocs=nprocs, nsteps
                                 =nsteps, ent_coef=ent_coef, vf_coef=vf_coef, vf_fisher_coef=
                                 vf_fisher_coef, lr=lr, max_grad_norm=max_grad_norm, kfac_clip=kfac_clip,
-                                lrschedule=lrschedule)
+                                lrschedule=lrschedule, is_async=is_async)
     if save_interval and logger.get_dir():
         import cloudpickle
         with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
             fh.write(cloudpickle.dumps(make_model))
     model = make_model()
 
+    if load_path is not None:
+        model.load(load_path)
+
     runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
     nbatch = nenvs*nsteps
     tstart = time.time()
     coord = tf.train.Coordinator()
-    enqueue_threads = model.q_runner.create_threads(model.sess, coord=coord, start=True)
+    if is_async:
+        enqueue_threads = model.q_runner.create_threads(model.sess, coord=coord, start=True)
+    else:
+        enqueue_threads = []
+
     for update in range(1, total_timesteps//nbatch+1):
         obs, states, rewards, masks, actions, values = runner.run()
         policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
@@ -152,4 +151,4 @@ def learn(policy, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval
             model.save(savepath)
     coord.request_stop()
     coord.join(enqueue_threads)
-    env.close()
+    return model

baselines/acktr/acktr_cont.py

@@ -1,142 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from baselines import logger
-import baselines.common as common
-from baselines.common import tf_util as U
-from baselines.acktr import kfac
-from baselines.common.filters import ZFilter
-
-def pathlength(path):
-    return path["reward"].shape[0]# Loss function that we'll differentiate to get the policy gradient
-
-def rollout(env, policy, max_pathlength, animate=False, obfilter=None):
-    """
-    Simulate the env and policy for max_pathlength steps
-    """
-    ob = env.reset()
-    prev_ob = np.float32(np.zeros(ob.shape))
-    if obfilter: ob = obfilter(ob)
-    terminated = False
-
-    obs = []
-    acs = []
-    ac_dists = []
-    logps = []
-    rewards = []
-    for _ in range(max_pathlength):
-        if animate:
-            env.render()
-        state = np.concatenate([ob, prev_ob], -1)
-        obs.append(state)
-        ac, ac_dist, logp = policy.act(state)
-        acs.append(ac)
-        ac_dists.append(ac_dist)
-        logps.append(logp)
-        prev_ob = np.copy(ob)
-        scaled_ac = env.action_space.low + (ac + 1.) * 0.5 * (env.action_space.high - env.action_space.low)
-        scaled_ac = np.clip(scaled_ac, env.action_space.low, env.action_space.high)
-        ob, rew, done, _ = env.step(scaled_ac)
-        if obfilter: ob = obfilter(ob)
-        rewards.append(rew)
-        if done:
-            terminated = True
-            break
-    return {"observation" : np.array(obs), "terminated" : terminated,
-            "reward" : np.array(rewards), "action" : np.array(acs),
-            "action_dist": np.array(ac_dists), "logp" : np.array(logps)}
-
-def learn(env, policy, vf, gamma, lam, timesteps_per_batch, num_timesteps,
-    animate=False, callback=None, desired_kl=0.002):
-
-    obfilter = ZFilter(env.observation_space.shape)
-
-    max_pathlength = env.spec.timestep_limit
-    stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)), name='stepsize')
-    inputs, loss, loss_sampled = policy.update_info
-    optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
-                                epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
-                                weight_decay_dict=policy.wd_dict, max_grad_norm=None)
-    pi_var_list = []
-    for var in tf.trainable_variables():
-        if "pi" in var.name:
-            pi_var_list.append(var)
-
-    update_op, q_runner = optim.minimize(loss, loss_sampled, var_list=pi_var_list)
-    do_update = U.function(inputs, update_op)
-    U.initialize()
-
-    # start queue runners
-    enqueue_threads = []
-    coord = tf.train.Coordinator()
-    for qr in [q_runner, vf.q_runner]:
-        assert (qr != None)
-        enqueue_threads.extend(qr.create_threads(tf.get_default_session(), coord=coord, start=True))
-
-    i = 0
-    timesteps_so_far = 0
-    while True:
-        if timesteps_so_far > num_timesteps:
-            break
-        logger.log("********** Iteration %i ************"%i)
-
-        # Collect paths until we have enough timesteps
-        timesteps_this_batch = 0
-        paths = []
-        while True:
-            path = rollout(env, policy, max_pathlength, animate=(len(paths)==0 and (i % 10 == 0) and animate), obfilter=obfilter)
-            paths.append(path)
-            n = pathlength(path)
-            timesteps_this_batch += n
-            timesteps_so_far += n
-            if timesteps_this_batch > timesteps_per_batch:
-                break
-
-        # Estimate advantage function
-        vtargs = []
-        advs = []
-        for path in paths:
-            rew_t = path["reward"]
-            return_t = common.discount(rew_t, gamma)
-            vtargs.append(return_t)
-            vpred_t = vf.predict(path)
-            vpred_t = np.append(vpred_t, 0.0 if path["terminated"] else vpred_t[-1])
-            delta_t = rew_t + gamma*vpred_t[1:] - vpred_t[:-1]
-            adv_t = common.discount(delta_t, gamma * lam)
-            advs.append(adv_t)
-        # Update value function
-        vf.fit(paths, vtargs)
-
-        # Build arrays for policy update
-        ob_no = np.concatenate([path["observation"] for path in paths])
-        action_na = np.concatenate([path["action"] for path in paths])
-        oldac_dist = np.concatenate([path["action_dist"] for path in paths])
-        adv_n = np.concatenate(advs)
-        standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
-
-        # Policy update
-        do_update(ob_no, action_na, standardized_adv_n)
-
-        min_stepsize = np.float32(1e-8)
-        max_stepsize = np.float32(1e0)
-        # Adjust stepsize
-        kl = policy.compute_kl(ob_no, oldac_dist)
-        if kl > desired_kl * 2:
-            logger.log("kl too high")
-            tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)).eval()
-        elif kl < desired_kl / 2:
-            logger.log("kl too low")
-            tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)).eval()
-        else:
-            logger.log("kl just right!")
-
-        logger.record_tabular("EpRewMean", np.mean([path["reward"].sum() for path in paths]))
-        logger.record_tabular("EpRewSEM", np.std([path["reward"].sum()/np.sqrt(len(paths)) for path in paths]))
-        logger.record_tabular("EpLenMean", np.mean([pathlength(path) for path in paths]))
-        logger.record_tabular("KL", kl)
-        if callback:
-            callback()
-        logger.dump_tabular()
-        i += 1
-
-    coord.request_stop()
-    coord.join(enqueue_threads)

baselines/acktr/defaults.py

@@ -0,0 +1,6 @@
+defaults = {
+    'mujoco' : dict(
+        nsteps=2500,
+        value_network='copy'
+    )
+}

baselines/acktr/kfac.py

@@ -1,6 +1,8 @@
 import tensorflow as tf
 import numpy as np
 import re
+
+ # flake8: noqa F403, F405
 from baselines.acktr.kfac_utils import *
 from functools import reduce
 
@@ -10,14 +12,14 @@ KFAC_DEBUG = False
 
 class KfacOptimizer():
 
-    def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60, full_stats_init=False, cold_iter=100, cold_lr=None, async=False, async_stats=False, epsilon=1e-2, stats_decay=0.95, blockdiag_bias=False, channel_fac=False, factored_damping=False, approxT2=False, use_float64=False, weight_decay_dict={},max_grad_norm=0.5):
+    def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60, full_stats_init=False, cold_iter=100, cold_lr=None, is_async=False, async_stats=False, epsilon=1e-2, stats_decay=0.95, blockdiag_bias=False, channel_fac=False, factored_damping=False, approxT2=False, use_float64=False, weight_decay_dict={},max_grad_norm=0.5):
         self.max_grad_norm = max_grad_norm
         self._lr = learning_rate
         self._momentum = momentum
         self._clip_kl = clip_kl
         self._channel_fac = channel_fac
         self._kfac_update = kfac_update
-        self._async = async
+        self._async = is_async
         self._async_stats = async_stats
         self._epsilon = epsilon
         self._stats_decay = stats_decay

baselines/acktr/policies.py

@@ -1,42 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from baselines.acktr.utils import dense, kl_div
-import baselines.common.tf_util as U
-
-class GaussianMlpPolicy(object):
-    def __init__(self, ob_dim, ac_dim):
-        # Here we'll construct a bunch of expressions, which will be used in two places:
-        # (1) When sampling actions
-        # (2) When computing loss functions, for the policy update
-        # Variables specific to (1) have the word "sampled" in them,
-        # whereas variables specific to (2) have the word "old" in them
-        ob_no = tf.placeholder(tf.float32, shape=[None, ob_dim*2], name="ob") # batch of observations
-        oldac_na = tf.placeholder(tf.float32, shape=[None, ac_dim], name="ac") # batch of actions previous actions
-        oldac_dist = tf.placeholder(tf.float32, shape=[None, ac_dim*2], name="oldac_dist") # batch of actions previous action distributions
-        adv_n = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage function estimate
-        wd_dict = {}
-        h1 = tf.nn.tanh(dense(ob_no, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
-        h2 = tf.nn.tanh(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0.0, weight_loss_dict=wd_dict))
-        mean_na = dense(h2, ac_dim, "mean", weight_init=U.normc_initializer(0.1), bias_init=0.0, weight_loss_dict=wd_dict) # Mean control output
-        self.wd_dict = wd_dict
-        self.logstd_1a = logstd_1a = tf.get_variable("logstd", [ac_dim], tf.float32, tf.zeros_initializer()) # Variance on outputs
-        logstd_1a = tf.expand_dims(logstd_1a, 0)
-        std_1a = tf.exp(logstd_1a)
-        std_na = tf.tile(std_1a, [tf.shape(mean_na)[0], 1])
-        ac_dist = tf.concat([tf.reshape(mean_na, [-1, ac_dim]), tf.reshape(std_na, [-1, ac_dim])], 1)
-        sampled_ac_na = tf.random_normal(tf.shape(ac_dist[:,ac_dim:])) * ac_dist[:,ac_dim:] + ac_dist[:,:ac_dim] # This is the sampled action we'll perform.
-        logprobsampled_n = - tf.reduce_sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * tf.reduce_sum(tf.square(ac_dist[:,:ac_dim] - sampled_ac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of sampled action
-        logprob_n = - tf.reduce_sum(tf.log(ac_dist[:,ac_dim:]), axis=1) - 0.5 * tf.log(2.0*np.pi)*ac_dim - 0.5 * tf.reduce_sum(tf.square(ac_dist[:,:ac_dim] - oldac_na) / (tf.square(ac_dist[:,ac_dim:])), axis=1) # Logprob of previous actions under CURRENT policy (whereas oldlogprob_n is under OLD policy)
-        kl = tf.reduce_mean(kl_div(oldac_dist, ac_dist, ac_dim))
-        #kl = .5 * tf.reduce_mean(tf.square(logprob_n - oldlogprob_n)) # Approximation of KL divergence between old policy used to generate actions, and new policy used to compute logprob_n
-        surr = - tf.reduce_mean(adv_n * logprob_n) # Loss function that we'll differentiate to get the policy gradient
-        surr_sampled = - tf.reduce_mean(logprob_n) # Sampled loss of the policy
-        self._act = U.function([ob_no], [sampled_ac_na, ac_dist, logprobsampled_n]) # Generate a new action and its logprob
-        #self.compute_kl = U.function([ob_no, oldac_na, oldlogprob_n], kl) # Compute (approximate) KL divergence between old policy and new policy
-        self.compute_kl = U.function([ob_no, oldac_dist], kl)
-        self.update_info = ((ob_no, oldac_na, adv_n), surr, surr_sampled) # Input and output variables needed for computing loss
-        U.initialize() # Initialize uninitialized TF variables
-
-    def act(self, ob):
-        ac, ac_dist, logp = self._act(ob[None])
-        return ac[0], ac_dist[0], logp[0]

baselines/acktr/run_atari.py

@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-
-from functools import partial
-
-from baselines import logger
-from baselines.acktr.acktr_disc import learn
-from baselines.common.cmd_util import make_atari_env, atari_arg_parser
-from baselines.common.vec_env.vec_frame_stack import VecFrameStack
-from baselines.ppo2.policies import CnnPolicy
-
-def train(env_id, num_timesteps, seed, num_cpu):
-    env = VecFrameStack(make_atari_env(env_id, num_cpu, seed), 4)
-    policy_fn = partial(CnnPolicy, one_dim_bias=True)
-    learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), nprocs=num_cpu)
-    env.close()
-
-def main():
-    args = atari_arg_parser().parse_args()
-    logger.configure()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, num_cpu=32)
-
-if __name__ == '__main__':
-    main()

baselines/acktr/run_mujoco.py

@@ -1,34 +0,0 @@
-#!/usr/bin/env python3
-
-import tensorflow as tf
-from baselines import logger
-from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
-from baselines.acktr.acktr_cont import learn
-from baselines.acktr.policies import GaussianMlpPolicy
-from baselines.acktr.value_functions import NeuralNetValueFunction
-
-def train(env_id, num_timesteps, seed):
-    env = make_mujoco_env(env_id, seed)
-
-    with tf.Session(config=tf.ConfigProto()):
-        ob_dim = env.observation_space.shape[0]
-        ac_dim = env.action_space.shape[0]
-        with tf.variable_scope("vf"):
-            vf = NeuralNetValueFunction(ob_dim, ac_dim)
-        with tf.variable_scope("pi"):
-            policy = GaussianMlpPolicy(ob_dim, ac_dim)
-
-        learn(env, policy=policy, vf=vf,
-            gamma=0.99, lam=0.97, timesteps_per_batch=2500,
-            desired_kl=0.002,
-            num_timesteps=num_timesteps, animate=False)
-
-        env.close()
-
-def main():
-    args = mujoco_arg_parser().parse_args()
-    logger.configure()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
-
-if __name__ == "__main__":
-    main()

baselines/acktr/value_functions.py

@@ -1,50 +0,0 @@
-from baselines import logger
-import numpy as np
-import baselines.common as common
-from baselines.common import tf_util as U
-import tensorflow as tf
-from baselines.acktr import kfac
-from baselines.acktr.utils import dense
-
-class NeuralNetValueFunction(object):
-    def __init__(self, ob_dim, ac_dim): #pylint: disable=W0613
-        X = tf.placeholder(tf.float32, shape=[None, ob_dim*2+ac_dim*2+2]) # batch of observations
-        vtarg_n = tf.placeholder(tf.float32, shape=[None], name='vtarg')
-        wd_dict = {}
-        h1 = tf.nn.elu(dense(X, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
-        h2 = tf.nn.elu(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
-        vpred_n = dense(h2, 1, "hfinal", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict)[:,0]
-        sample_vpred_n = vpred_n + tf.random_normal(tf.shape(vpred_n))
-        wd_loss = tf.get_collection("vf_losses", None)
-        loss = tf.reduce_mean(tf.square(vpred_n - vtarg_n)) + tf.add_n(wd_loss)
-        loss_sampled = tf.reduce_mean(tf.square(vpred_n - tf.stop_gradient(sample_vpred_n)))
-        self._predict = U.function([X], vpred_n)
-        optim = kfac.KfacOptimizer(learning_rate=0.001, cold_lr=0.001*(1-0.9), momentum=0.9, \
-                                    clip_kl=0.3, epsilon=0.1, stats_decay=0.95, \
-                                    async=1, kfac_update=2, cold_iter=50, \
-                                    weight_decay_dict=wd_dict, max_grad_norm=None)
-        vf_var_list = []
-        for var in tf.trainable_variables():
-            if "vf" in var.name:
-                vf_var_list.append(var)
-
-        update_op, self.q_runner = optim.minimize(loss, loss_sampled, var_list=vf_var_list)
-        self.do_update = U.function([X, vtarg_n], update_op) #pylint: disable=E1101
-        U.initialize() # Initialize uninitialized TF variables
-    def _preproc(self, path):
-        l = pathlength(path)
-        al = np.arange(l).reshape(-1,1)/10.0
-        act = path["action_dist"].astype('float32')
-        X = np.concatenate([path['observation'], act, al, np.ones((l, 1))], axis=1)
-        return X
-    def predict(self, path):
-        return self._predict(self._preproc(path))
-    def fit(self, paths, targvals):
-        X = np.concatenate([self._preproc(p) for p in paths])
-        y = np.concatenate(targvals)
-        logger.record_tabular("EVBefore", common.explained_variance(self._predict(X), y))
-        for _ in range(25): self.do_update(X, y)
-        logger.record_tabular("EVAfter", common.explained_variance(self._predict(X), y))
-
-def pathlength(path):
-    return path["reward"].shape[0]

baselines/bench/__init__.py

@@ -1,2 +1,2 @@
 from baselines.bench.benchmarks import *
-from baselines.bench.monitor import *
+from baselines.bench.monitor import *

baselines/bench/benchmarks.py

@@ -59,7 +59,7 @@ register_benchmark({
 register_benchmark({
     'name': 'Atari10M',
     'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 10M timesteps',
-    'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(10e6)} for _game in _atari7]
+    'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 6, 'num_timesteps': int(10e6)} for _game in _atari7]
 })
 
 register_benchmark({
@@ -84,8 +84,9 @@ _mujocosmall = [
 register_benchmark({
     'name': 'Mujoco1M',
     'description': 'Some small 2D MuJoCo tasks, run for 1M timesteps',
-    'tasks': [{'env_id': _envid, 'trials': 3, 'num_timesteps': int(1e6)} for _envid in _mujocosmall]
+    'tasks': [{'env_id': _envid, 'trials': 6, 'num_timesteps': int(1e6)} for _envid in _mujocosmall]
 })
+
 register_benchmark({
     'name': 'MujocoWalkers',
     'description': 'MuJoCo forward walkers, run for 8M, humanoid 100M',
@@ -96,6 +97,19 @@ register_benchmark({
     ]
 })
 
+# Bullet
+_bulletsmall = [
+    'InvertedDoublePendulum', 'InvertedPendulum', 'HalfCheetah', 'Reacher', 'Walker2D', 'Hopper', 'Ant'
+]
+_bulletsmall = [e + 'BulletEnv-v0' for e in _bulletsmall]
+
+register_benchmark({
+    'name': 'Bullet1M',
+    'description': '6 mujoco-like tasks from bullet, 1M steps',
+    'tasks': [{'env_id': e, 'trials': 6, 'num_timesteps': int(1e6)} for e in _bulletsmall]
+})
+
+
 # Roboschool
 
 register_benchmark({

baselines/bench/monitor.py

@@ -16,21 +16,11 @@ class Monitor(Wrapper):
     def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
         Wrapper.__init__(self, env=env)
         self.tstart = time.time()
-        if filename is None:
-            self.f = None
-            self.logger = None
-        else:
-            if not filename.endswith(Monitor.EXT):
-                if osp.isdir(filename):
-                    filename = osp.join(filename, Monitor.EXT)
-                else:
-                    filename = filename + "." + Monitor.EXT
-            self.f = open(filename, "wt")
-            self.f.write('#%s\n'%json.dumps({"t_start": self.tstart, 'env_id' : env.spec and env.spec.id}))
-            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+reset_keywords+info_keywords)
-            self.logger.writeheader()
-            self.f.flush()
-
+        self.results_writer = ResultsWriter(
+            filename,
+            header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
+            extra_keys=reset_keywords + info_keywords
+        )
         self.reset_keywords = reset_keywords
         self.info_keywords = info_keywords
         self.allow_early_resets = allow_early_resets
@@ -43,10 +33,7 @@ class Monitor(Wrapper):
         self.current_reset_info = {} # extra info about the current episode, that was passed in during reset()
 
     def reset(self, **kwargs):
-        if not self.allow_early_resets and not self.needs_reset:
-            raise RuntimeError("Tried to reset an environment before done. If you want to allow early resets, wrap your env with Monitor(env, path, allow_early_resets=True)")
-        self.rewards = []
-        self.needs_reset = False
+        self.reset_state()
         for k in self.reset_keywords:
             v = kwargs.get(k)
             if v is None:
@@ -54,10 +41,21 @@ class Monitor(Wrapper):
             self.current_reset_info[k] = v
         return self.env.reset(**kwargs)
 
+    def reset_state(self):
+        if not self.allow_early_resets and not self.needs_reset:
+            raise RuntimeError("Tried to reset an environment before done. If you want to allow early resets, wrap your env with Monitor(env, path, allow_early_resets=True)")
+        self.rewards = []
+        self.needs_reset = False
+
+
     def step(self, action):
         if self.needs_reset:
             raise RuntimeError("Tried to step environment that needs reset")
         ob, rew, done, info = self.env.step(action)
+        self.update(ob, rew, done, info)
+        return (ob, rew, done, info)
+
+    def update(self, ob, rew, done, info):
         self.rewards.append(rew)
         if done:
             self.needs_reset = True
@@ -70,12 +68,12 @@ class Monitor(Wrapper):
             self.episode_lengths.append(eplen)
             self.episode_times.append(time.time() - self.tstart)
             epinfo.update(self.current_reset_info)
-            if self.logger:
-                self.logger.writerow(epinfo)
-                self.f.flush()
-            info['episode'] = epinfo
+            self.results_writer.write_row(epinfo)
+
+            if isinstance(info, dict):
+                info['episode'] = epinfo
+
         self.total_steps += 1
-        return (ob, rew, done, info)
 
     def close(self):
         if self.f is not None:
@@ -96,13 +94,41 @@ class Monitor(Wrapper):
 class LoadMonitorResultsError(Exception):
     pass
 
+
+class ResultsWriter(object):
+    def __init__(self, filename=None, header='', extra_keys=()):
+        self.extra_keys = extra_keys
+        if filename is None:
+            self.f = None
+            self.logger = None
+        else:
+            if not filename.endswith(Monitor.EXT):
+                if osp.isdir(filename):
+                    filename = osp.join(filename, Monitor.EXT)
+                else:
+                    filename = filename + "." + Monitor.EXT
+            self.f = open(filename, "wt")
+            if isinstance(header, dict):
+                header = '# {} \n'.format(json.dumps(header))
+            self.f.write(header)
+            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
+            self.logger.writeheader()
+            self.f.flush()
+
+    def write_row(self, epinfo):
+        if self.logger:
+            self.logger.writerow(epinfo)
+            self.f.flush()
+
+
+
 def get_monitor_files(dir):
     return glob(osp.join(dir, "*" + Monitor.EXT))
 
 def load_results(dir):
     import pandas
     monitor_files = (
-        glob(osp.join(dir, "*monitor.json")) + 
+        glob(osp.join(dir, "*monitor.json")) +
         glob(osp.join(dir, "*monitor.csv"))) # get both csv and (old) json files
     if not monitor_files:
         raise LoadMonitorResultsError("no monitor files of the form *%s found in %s" % (Monitor.EXT, dir))
@@ -112,6 +138,8 @@ def load_results(dir):
         with open(fname, 'rt') as fh:
             if fname.endswith('csv'):
                 firstline = fh.readline()
+                if not firstline:
+                    continue
                 assert firstline[0] == '#'
                 header = json.loads(firstline[1:])
                 df = pandas.read_csv(fh, index_col=None)
@@ -158,4 +186,4 @@ def test_monitor():
     last_logline = pandas.read_csv(f, index_col=None)
     assert set(last_logline.keys()) == {'l', 't', 'r'}, "Incorrect keys in monitor logline"
     f.close()
-    os.remove(mon_file)
+    os.remove(mon_file)

baselines/common/atari_wrappers.py

@@ -1,4 +1,6 @@
 import numpy as np
+import os
+os.environ.setdefault('PATH', '')
 from collections import deque
 import gym
 from gym import spaces
@@ -154,7 +156,7 @@ class FrameStack(gym.Wrapper):
         self.k = k
         self.frames = deque([], maxlen=k)
         shp = env.observation_space.shape
-        self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=np.uint8)
+        self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=env.observation_space.dtype)
 
     def reset(self):
         ob = self.env.reset()
@@ -174,6 +176,7 @@ class FrameStack(gym.Wrapper):
 class ScaledFloatFrame(gym.ObservationWrapper):
     def __init__(self, env):
         gym.ObservationWrapper.__init__(self, env)
+        self.observation_space = gym.spaces.Box(low=0, high=1, shape=env.observation_space.shape, dtype=np.float32)
 
     def observation(self, observation):
         # careful! This undoes the memory optimization, use
@@ -210,8 +213,11 @@ class LazyFrames(object):
     def __getitem__(self, i):
         return self._force()[i]
 
-def make_atari(env_id):
+def make_atari(env_id, timelimit=True):
+    # XXX(john): remove timelimit argument after gym is upgraded to allow double wrapping
     env = gym.make(env_id)
+    if not timelimit:
+        env = env.env
     assert 'NoFrameskip' in env.spec.id
     env = NoopResetEnv(env, noop_max=30)
     env = MaxAndSkipEnv(env, skip=4)

baselines/common/cg.py

@@ -31,4 +31,4 @@ def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
     if callback is not None:
         callback(x)
     if verbose: print(fmtstr % (i+1, rdotr, np.linalg.norm(x)))  # pylint: disable=W0631
-    return x
+    return x

baselines/common/cmd_util.py

@@ -3,7 +3,11 @@ Helpers for scripts like run_atari.py.
 """
 
 import os
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 import gym
 from gym.wrappers import FlattenDictWrapper
 from baselines import logger
@@ -11,31 +15,81 @@ from baselines.bench import Monitor
 from baselines.common import set_global_seeds
 from baselines.common.atari_wrappers import make_atari, wrap_deepmind
 from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 
-def make_atari_env(env_id, num_env, seed, wrapper_kwargs=None, start_index=0):
+from baselines.common import retro_wrappers
+
+def make_vec_env(env_id, env_type, num_env, seed, wrapper_kwargs=None, start_index=0, reward_scale=1.0, gamestate=None, frame_stack_size=1):
     """
-    Create a wrapped, monitored SubprocVecEnv for Atari.
+    Create a wrapped, monitored SubprocVecEnv
     """
     if wrapper_kwargs is None: wrapper_kwargs = {}
-    def make_env(rank): # pylint: disable=C0111
-        def _thunk():
-            env = make_atari(env_id)
-            env.seed(seed + rank)
-            env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
-            return wrap_deepmind(env, **wrapper_kwargs)
-        return _thunk
-    set_global_seeds(seed)
-    return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
+    mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+    seed = seed + 10000 * mpi_rank if seed is not None else None
+    def make_thunk(rank):
+        return lambda: make_env(
+            env_id=env_id,
+            env_type=env_type,
+            subrank = rank,
+            seed=seed,
+            reward_scale=reward_scale,
+            gamestate=gamestate,
+            wrapper_kwargs=wrapper_kwargs
+        )
 
-def make_mujoco_env(env_id, seed):
+    set_global_seeds(seed)
+    if num_env > 1:
+        venv = SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
+    else:
+        venv = DummyVecEnv([make_thunk(start_index)])
+
+    if frame_stack_size > 1:
+        venv = VecFrameStack(venv, frame_stack_size)
+
+    return venv
+
+
+
+def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate=None, wrapper_kwargs={}):
+    mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+    if env_type == 'atari':
+        env = make_atari(env_id)
+    elif env_type == 'retro':
+        import retro
+        gamestate = gamestate or retro.State.DEFAULT
+        env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
+    else:
+        env = gym.make(env_id)
+
+    env.seed(seed + subrank if seed is not None else None)
+    env = Monitor(env,
+                  logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
+                  allow_early_resets=True)
+
+    if env_type == 'atari':
+         return wrap_deepmind(env, **wrapper_kwargs)
+    elif reward_scale != 1:
+         return retro_wrappers.RewardScaler(env, reward_scale)
+    else:
+        return env
+
+
+
+def make_mujoco_env(env_id, seed, reward_scale=1.0):
     """
     Create a wrapped, monitored gym.Env for MuJoCo.
     """
     rank = MPI.COMM_WORLD.Get_rank()
-    set_global_seeds(seed + 10000 * rank)
+    myseed = seed  + 1000 * rank if seed is not None else None
+    set_global_seeds(myseed)
     env = gym.make(env_id)
-    env = Monitor(env, os.path.join(logger.get_dir(), str(rank)))
+    logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
+    env = Monitor(env, logger_path, allow_early_resets=True)
     env.seed(seed)
+    if reward_scale != 1.0:
+        from baselines.common.retro_wrappers import RewardScaler
+        env = RewardScaler(env, reward_scale)
     return env
 
 def make_robotics_env(env_id, seed, rank=0):
@@ -62,20 +116,27 @@ def atari_arg_parser():
     """
     Create an argparse.ArgumentParser for run_atari.py.
     """
-    parser = arg_parser()
-    parser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
-    parser.add_argument('--num-timesteps', type=int, default=int(10e6))
-    return parser
+    print('Obsolete - use common_arg_parser instead')
+    return common_arg_parser()
 
 def mujoco_arg_parser():
+    print('Obsolete - use common_arg_parser instead')
+    return common_arg_parser()
+
+def common_arg_parser():
     """
     Create an argparse.ArgumentParser for run_mujoco.py.
     """
     parser = arg_parser()
     parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
-    parser.add_argument('--num-timesteps', type=int, default=int(1e6))
+    parser.add_argument('--seed', help='RNG seed', type=int, default=None)
+    parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
+    parser.add_argument('--num_timesteps', type=float, default=1e6),
+    parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None)
+    parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None)
+    parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
+    parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
+    parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
     parser.add_argument('--play', default=False, action='store_true')
     return parser
 
@@ -85,6 +146,28 @@ def robotics_arg_parser():
     """
     parser = arg_parser()
     parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
+    parser.add_argument('--seed', help='RNG seed', type=int, default=None)
     parser.add_argument('--num-timesteps', type=int, default=int(1e6))
     return parser
+
+
+def parse_unknown_args(args):
+    """
+    Parse arguments not consumed by arg parser into a dicitonary
+    """
+    retval = {}
+    preceded_by_key = False
+    for arg in args:
+        if arg.startswith('--'):
+            if '=' in arg:
+                key = arg.split('=')[0][2:]
+                value = arg.split('=')[1]
+                retval[key] = value
+            else:
+                key = arg[2:]
+                preceded_by_key = True
+        elif preceded_by_key:
+            retval[key] = arg
+            preceded_by_key = False
+
+    return retval

baselines/common/console_util.py

@@ -2,6 +2,8 @@ from __future__ import print_function
 from contextlib import contextmanager
 import numpy as np
 import time
+import shlex
+import subprocess
 
 # ================================================================
 # Misc
@@ -37,7 +39,7 @@ color2num = dict(
     crimson=38
 )
 
-def colorize(string, color, bold=False, highlight=False):
+def colorize(string, color='green', bold=False, highlight=False):
     attr = []
     num = color2num[color]
     if highlight: num += 10
@@ -45,6 +47,25 @@ def colorize(string, color, bold=False, highlight=False):
     if bold: attr.append('1')
     return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string)
 
+def print_cmd(cmd, dry=False):
+    if isinstance(cmd, str):  # for shell=True
+        pass
+    else:
+        cmd = ' '.join(shlex.quote(arg) for arg in cmd)
+    print(colorize(('CMD: ' if not dry else 'DRY: ') + cmd))
+
+
+def get_git_commit(cwd=None):
+    return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=cwd).decode('utf8')
+
+def get_git_commit_message(cwd=None):
+    return subprocess.check_output(['git', 'show', '-s', '--format=%B', 'HEAD'], cwd=cwd).decode('utf8')
+
+def ccap(cmd, dry=False, env=None, **kwargs):
+    print_cmd(cmd, dry)
+    if not dry:
+        subprocess.check_call(cmd, env=env, **kwargs)
+
 
 MESSAGE_DEPTH = 0
 

baselines/common/distributions.py

@@ -23,6 +23,13 @@ class Pd(object):
         raise NotImplementedError
     def logp(self, x):
         return - self.neglogp(x)
+    def get_shape(self):
+        return self.flatparam().shape
+    @property
+    def shape(self):
+        return self.get_shape()
+    def __getitem__(self, idx):
+        return self.__class__(self.flatparam()[idx])
 
 class PdType(object):
     """
@@ -46,6 +53,9 @@ class PdType(object):
     def sample_placeholder(self, prepend_shape, name=None):
         return tf.placeholder(dtype=self.sample_dtype(), shape=prepend_shape+self.sample_shape(), name=name)
 
+    def __eq__(self, other):
+        return (type(self) == type(other)) and (self.__dict__ == other.__dict__)
+
 class CategoricalPdType(PdType):
     def __init__(self, ncat):
         self.ncat = ncat
@@ -85,7 +95,7 @@ class DiagGaussianPdType(PdType):
 
     def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
         mean = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
-        logstd = tf.get_variable(name='logstd', shape=[1, self.size], initializer=tf.zeros_initializer())
+        logstd = tf.get_variable(name='pi/logstd', shape=[1, self.size], initializer=tf.zeros_initializer())
         pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
         return self.pdfromflat(pdparam), mean
 
@@ -107,6 +117,9 @@ class BernoulliPdType(PdType):
         return [self.size]
     def sample_dtype(self):
         return tf.int32
+    def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
+        pdparam = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
+        return self.pdfromflat(pdparam), pdparam
 
 # WRONG SECOND DERIVATIVES
 # class CategoricalPd(Pd):
@@ -138,31 +151,47 @@ class CategoricalPd(Pd):
         return self.logits
     def mode(self):
         return tf.argmax(self.logits, axis=-1)
+
+    @property
+    def mean(self):
+        return tf.nn.softmax(self.logits)
     def neglogp(self, x):
         # return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
         # Note: we can't use sparse_softmax_cross_entropy_with_logits because
         #       the implementation does not allow second-order derivatives...
-        one_hot_actions = tf.one_hot(x, self.logits.get_shape().as_list()[-1])
-        return tf.nn.softmax_cross_entropy_with_logits(
+        if x.dtype in {tf.uint8, tf.int32, tf.int64}:
+            # one-hot encoding
+            x_shape_list = x.shape.as_list()
+            logits_shape_list = self.logits.get_shape().as_list()[:-1]
+            for xs, ls in zip(x_shape_list, logits_shape_list):
+                if xs is not None and ls is not None:
+                    assert xs == ls, 'shape mismatch: {} in x vs {} in logits'.format(xs, ls)
+
+            x = tf.one_hot(x, self.logits.get_shape().as_list()[-1])
+        else:
+            # already encoded
+            assert x.shape.as_list() == self.logits.shape.as_list()
+
+        return tf.nn.softmax_cross_entropy_with_logits_v2(
             logits=self.logits,
-            labels=one_hot_actions)
+            labels=x)
     def kl(self, other):
-        a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keep_dims=True)
-        a1 = other.logits - tf.reduce_max(other.logits, axis=-1, keep_dims=True)
+        a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
+        a1 = other.logits - tf.reduce_max(other.logits, axis=-1, keepdims=True)
         ea0 = tf.exp(a0)
         ea1 = tf.exp(a1)
-        z0 = tf.reduce_sum(ea0, axis=-1, keep_dims=True)
-        z1 = tf.reduce_sum(ea1, axis=-1, keep_dims=True)
+        z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
+        z1 = tf.reduce_sum(ea1, axis=-1, keepdims=True)
         p0 = ea0 / z0
         return tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=-1)
     def entropy(self):
-        a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keep_dims=True)
+        a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
         ea0 = tf.exp(a0)
-        z0 = tf.reduce_sum(ea0, axis=-1, keep_dims=True)
+        z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
         p0 = ea0 / z0
         return tf.reduce_sum(p0 * (tf.log(z0) - a0), axis=-1)
     def sample(self):
-        u = tf.random_uniform(tf.shape(self.logits))
+        u = tf.random_uniform(tf.shape(self.logits), dtype=self.logits.dtype)
         return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=-1)
     @classmethod
     def fromflat(cls, flat):
@@ -214,12 +243,16 @@ class DiagGaussianPd(Pd):
     def fromflat(cls, flat):
         return cls(flat)
 
+
 class BernoulliPd(Pd):
     def __init__(self, logits):
         self.logits = logits
         self.ps = tf.sigmoid(logits)
     def flatparam(self):
         return self.logits
+    @property
+    def mean(self):
+        return self.ps
     def mode(self):
         return tf.round(self.ps)
     def neglogp(self, x):

baselines/common/filters.py

@@ -1,98 +0,0 @@
-from .running_stat import RunningStat
-from collections import deque
-import numpy as np
-
-class Filter(object):
-    def __call__(self, x, update=True):
-        raise NotImplementedError
-    def reset(self):
-        pass
-
-class IdentityFilter(Filter):
-    def __call__(self, x, update=True):
-        return x
-
-class CompositionFilter(Filter):
-    def __init__(self, fs):
-        self.fs = fs
-    def __call__(self, x, update=True):
-        for f in self.fs:
-            x = f(x)
-        return x
-    def output_shape(self, input_space):
-        out = input_space.shape
-        for f in self.fs:
-            out = f.output_shape(out)
-        return out
-
-class ZFilter(Filter):
-    """
-    y = (x-mean)/std
-    using running estimates of mean,std
-    """
-
-    def __init__(self, shape, demean=True, destd=True, clip=10.0):
-        self.demean = demean
-        self.destd = destd
-        self.clip = clip
-
-        self.rs = RunningStat(shape)
-
-    def __call__(self, x, update=True):
-        if update: self.rs.push(x)
-        if self.demean:
-            x = x - self.rs.mean
-        if self.destd:
-            x = x / (self.rs.std+1e-8)
-        if self.clip:
-            x = np.clip(x, -self.clip, self.clip)
-        return x
-    def output_shape(self, input_space):
-        return input_space.shape
-
-class AddClock(Filter):
-    def __init__(self):
-        self.count = 0
-    def reset(self):
-        self.count = 0
-    def __call__(self, x, update=True):
-        return np.append(x, self.count/100.0)
-    def output_shape(self, input_space):
-        return (input_space.shape[0]+1,)
-
-class FlattenFilter(Filter):
-    def __call__(self, x, update=True):
-        return x.ravel()
-    def output_shape(self, input_space):
-        return (int(np.prod(input_space.shape)),)
-
-class Ind2OneHotFilter(Filter):
-    def __init__(self, n):
-        self.n = n
-    def __call__(self, x, update=True):
-        out = np.zeros(self.n)
-        out[x] = 1
-        return out
-    def output_shape(self, input_space):
-        return (input_space.n,)
-
-class DivFilter(Filter):
-    def __init__(self, divisor):
-        self.divisor = divisor
-    def __call__(self, x, update=True):
-        return x / self.divisor
-    def output_shape(self, input_space):
-        return input_space.shape
-
-class StackFilter(Filter):
-    def __init__(self, length):
-        self.stack = deque(maxlen=length)
-    def reset(self):
-        self.stack.clear()
-    def __call__(self, x, update=True):
-        self.stack.append(x)
-        while len(self.stack) < self.stack.maxlen:
-            self.stack.append(x)
-        return np.concatenate(self.stack, axis=-1)
-    def output_shape(self, input_space):
-        return input_space.shape[:-1] + (input_space.shape[-1] * self.stack.maxlen,)

baselines/common/identity_env.py

@@ -1,30 +0,0 @@
-from gym import Env
-from gym.spaces import Discrete
-
-
-class IdentityEnv(Env):
-    def __init__(
-            self,
-            dim,
-            ep_length=100,
-    ):
-
-        self.action_space = Discrete(dim)
-        self.reset()
-
-    def reset(self):
-        self._choose_next_state()
-        self.observation_space = self.action_space
-
-        return self.state
-
-    def step(self, actions):
-        rew = self._get_reward(actions)
-        self._choose_next_state()
-        return self.state, rew, False, {}
-
-    def _choose_next_state(self):
-        self.state = self.action_space.sample()
-
-    def _get_reward(self, actions):
-        return 1 if self.state == actions else 0

baselines/common/input.py

@@ -1,30 +1,56 @@
 import tensorflow as tf
 from gym.spaces import Discrete, Box
 
+def observation_placeholder(ob_space, batch_size=None, name='Ob'):
+    '''
+    Create placeholder to feed observations into of the size appropriate to the observation space
+
+    Parameters:
+    ----------
+
+    ob_space: gym.Space     observation space
+
+    batch_size: int         size of the batch to be fed into input. Can be left None in most cases.
+
+    name: str               name of the placeholder
+
+    Returns:
+    -------
+
+    tensorflow placeholder tensor
+    '''
+
+    assert isinstance(ob_space, Discrete) or isinstance(ob_space, Box), \
+        'Can only deal with Discrete and Box observation spaces for now'
+
+    return tf.placeholder(shape=(batch_size,) + ob_space.shape, dtype=ob_space.dtype, name=name)
+
+
 def observation_input(ob_space, batch_size=None, name='Ob'):
     '''
-    Build observation input with encoding depending on the 
-    observation space type
-    Params:
-    
-    ob_space: observation space (should be one of gym.spaces)
-    batch_size: batch size for input (default is None, so that resulting input placeholder can take tensors with any batch size)
-    name: tensorflow variable name for input placeholder
+    Create placeholder to feed observations into of the size appropriate to the observation space, and add input
+    encoder of the appropriate type.
+    '''
 
-    returns: tuple (input_placeholder, processed_input_tensor)
+    placeholder = observation_placeholder(ob_space, batch_size, name)
+    return placeholder, encode_observation(ob_space, placeholder)
+
+def encode_observation(ob_space, placeholder):
+    '''
+    Encode input in the way that is appropriate to the observation space
+
+    Parameters:
+    ----------
+
+    ob_space: gym.Space             observation space
+
+    placeholder: tf.placeholder     observation input placeholder
     '''
     if isinstance(ob_space, Discrete):
-        input_x  = tf.placeholder(shape=(batch_size,), dtype=tf.int32, name=name)
-        processed_x = tf.to_float(tf.one_hot(input_x, ob_space.n))
-        return input_x, processed_x
+        return tf.to_float(tf.one_hot(placeholder, ob_space.n))
 
     elif isinstance(ob_space, Box):
-        input_shape = (batch_size,) + ob_space.shape
-        input_x = tf.placeholder(shape=input_shape, dtype=ob_space.dtype, name=name)
-        processed_x = tf.to_float(input_x)
-        return input_x, processed_x
-
+        return tf.to_float(placeholder)
     else:
         raise NotImplementedError
 
- 

baselines/common/math_util.py

@@ -82,4 +82,4 @@ def test_discount_with_boundaries():
         2 + gamma * 3,
         3,
         4
-    ])
+    ])

baselines/common/misc_util.py

@@ -67,14 +67,20 @@ class EzPickle(object):
 
 
 def set_global_seeds(i):
+    try:
+        import MPI
+        rank = MPI.COMM_WORLD.Get_rank()
+    except ImportError:
+        rank = 0
+
+    myseed = i  + 1000 * rank if i is not None else None
     try:
         import tensorflow as tf
+        tf.set_random_seed(myseed)
     except ImportError:
         pass
-    else:
-        tf.set_random_seed(i)
-    np.random.seed(i)
-    random.seed(i)
+    np.random.seed(myseed)
+    random.seed(myseed)
 
 
 def pretty_eta(seconds_left):

baselines/common/models.py

@@ -0,0 +1,224 @@
+import numpy as np
+import tensorflow as tf
+from baselines.a2c import utils
+from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+import tensorflow.contrib.layers as layers
+
+mapping = {}
+
+def register(name):
+    def _thunk(func):
+        mapping[name] = func
+        return func
+    return _thunk
+
+def nature_cnn(unscaled_images, **conv_kwargs):
+    """
+    CNN from Nature paper.
+    """
+    scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
+    activ = tf.nn.relu
+    h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
+                   **conv_kwargs))
+    h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
+    h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
+    h3 = conv_to_fc(h3)
+    return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
+
+
+@register("mlp")
+def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):
+    """
+    Stack of fully-connected layers to be used in a policy / q-function approximator
+
+    Parameters:
+    ----------
+
+    num_layers: int                 number of fully-connected layers (default: 2)
+
+    num_hidden: int                 size of fully-connected layers (default: 64)
+
+    activation:                     activation function (default: tf.tanh)
+
+    Returns:
+    -------
+
+    function that builds fully connected network with a given input tensor / placeholder
+    """
+    def network_fn(X):
+        h = tf.layers.flatten(X)
+        for i in range(num_layers):
+            h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
+            if layer_norm:
+                h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
+            h = activation(h)
+
+        return h
+
+    return network_fn
+
+
+@register("cnn")
+def cnn(**conv_kwargs):
+    def network_fn(X):
+        return nature_cnn(X, **conv_kwargs)
+    return network_fn
+
+
+@register("cnn_small")
+def cnn_small(**conv_kwargs):
+    def network_fn(X):
+        h = tf.cast(X, tf.float32) / 255.
+
+        activ = tf.nn.relu
+        h = activ(conv(h, 'c1', nf=8, rf=8, stride=4, init_scale=np.sqrt(2), **conv_kwargs))
+        h = activ(conv(h, 'c2', nf=16, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
+        h = conv_to_fc(h)
+        h = activ(fc(h, 'fc1', nh=128, init_scale=np.sqrt(2)))
+        return h
+    return network_fn
+
+
+@register("lstm")
+def lstm(nlstm=128, layer_norm=False):
+    """
+    Builds LSTM (Long-Short Term Memory) network to be used in a policy.
+    Note that the resulting function returns not only the output of the LSTM
+    (i.e. hidden state of lstm for each step in the sequence), but also a dictionary
+    with auxiliary tensors to be set as policy attributes.
+
+    Specifically,
+        S is a placeholder to feed current state (LSTM state has to be managed outside policy)
+        M is a placeholder for the mask (used to mask out observations after the end of the episode, but can be used for other purposes too)
+        initial_state is a numpy array containing initial lstm state (usually zeros)
+        state is the output LSTM state (to be fed into S at the next call)
+
+
+    An example of usage of lstm-based policy can be found here: common/tests/test_doc_examples.py/test_lstm_example
+
+    Parameters:
+    ----------
+
+    nlstm: int          LSTM hidden state size
+
+    layer_norm: bool    if True, layer-normalized version of LSTM is used
+
+    Returns:
+    -------
+
+    function that builds LSTM with a given input tensor / placeholder
+    """
+
+    def network_fn(X, nenv=1):
+        nbatch = X.shape[0]
+        nsteps = nbatch // nenv
+
+        h = tf.layers.flatten(X)
+
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+        S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states
+
+        xs = batch_to_seq(h, nenv, nsteps)
+        ms = batch_to_seq(M, nenv, nsteps)
+
+        if layer_norm:
+            h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
+        else:
+            h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)
+
+        h = seq_to_batch(h5)
+        initial_state = np.zeros(S.shape.as_list(), dtype=float)
+
+        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
+
+    return network_fn
+
+
+@register("cnn_lstm")
+def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
+    def network_fn(X, nenv=1):
+        nbatch = X.shape[0]
+        nsteps = nbatch // nenv
+
+        h = nature_cnn(X, **conv_kwargs)
+
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+        S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states
+
+        xs = batch_to_seq(h, nenv, nsteps)
+        ms = batch_to_seq(M, nenv, nsteps)
+
+        if layer_norm:
+            h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
+        else:
+            h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)
+
+        h = seq_to_batch(h5)
+        initial_state = np.zeros(S.shape.as_list(), dtype=float)
+
+        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
+
+    return network_fn
+
+
+@register("cnn_lnlstm")
+def cnn_lnlstm(nlstm=128, **conv_kwargs):
+    return cnn_lstm(nlstm, layer_norm=True, **conv_kwargs)
+
+
+@register("conv_only")
+def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
+    '''
+    convolutions-only net
+
+    Parameters:
+    ----------
+
+    conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
+
+    Returns:
+
+    function that takes tensorflow tensor as input and returns the output of the last convolutional layer
+
+    '''
+
+    def network_fn(X):
+        out = tf.cast(X, tf.float32) / 255.
+        with tf.variable_scope("convnet"):
+            for num_outputs, kernel_size, stride in convs:
+                out = layers.convolution2d(out,
+                                           num_outputs=num_outputs,
+                                           kernel_size=kernel_size,
+                                           stride=stride,
+                                           activation_fn=tf.nn.relu,
+                                           **conv_kwargs)
+
+        return out
+    return network_fn
+
+def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]):
+    rms = RunningMeanStd(shape=x.shape[1:])
+    norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range))
+    return norm_x, rms
+
+
+def get_network_builder(name):
+    """
+    If you want to register your own network outside models.py, you just need:
+
+    Usage Example:
+    -------------
+    from baselines.common.models import register
+    @register("your_network_name")
+    def your_network_define(**net_kwargs):
+        ...
+        return network_fn
+
+    """
+    if callable(name):
+        return name
+    elif name in mapping:
+        return mapping[name]
+    else:
+        raise ValueError('Unknown network type: {}'.format(name))

baselines/common/mpi_adam.py

@@ -76,4 +76,4 @@ def test_MpiAdam():
     for i in range(10):
         l,g = lossandgrad()
         adam.update(g, stepsize)
-        print(i,l)
+        print(i,l)

baselines/common/mpi_adam_optimizer.py

@@ -0,0 +1,31 @@
+import numpy as np
+import tensorflow as tf
+from mpi4py import MPI
+
+class MpiAdamOptimizer(tf.train.AdamOptimizer):
+    """Adam optimizer that averages gradients across mpi processes."""
+    def __init__(self, comm, **kwargs):
+        self.comm = comm
+        tf.train.AdamOptimizer.__init__(self, **kwargs)
+    def compute_gradients(self, loss, var_list, **kwargs):
+        grads_and_vars = tf.train.AdamOptimizer.compute_gradients(self, loss, var_list, **kwargs)
+        grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]
+        flat_grad = tf.concat([tf.reshape(g, (-1,)) for g, v in grads_and_vars], axis=0)
+        shapes = [v.shape.as_list() for g, v in grads_and_vars]
+        sizes = [int(np.prod(s)) for s in shapes]
+
+        num_tasks = self.comm.Get_size()
+        buf = np.zeros(sum(sizes), np.float32)
+
+        def _collect_grads(flat_grad):
+            self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
+            np.divide(buf, float(num_tasks), out=buf)
+            return buf
+
+        avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
+        avg_flat_grad.set_shape(flat_grad.shape)
+        avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
+        avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
+                    for g, (_, v) in zip(avg_grads, grads_and_vars)]
+
+        return avg_grads_and_vars

baselines/common/mpi_fork.py

@@ -4,7 +4,7 @@ def mpi_fork(n, bind_to_core=False):
     """Re-launches the current script with workers
     Returns "parent" for original parent, "child" for MPI children
     """
-    if n<=1: 
+    if n<=1:
         return "child"
     if os.getenv("IN_MPI") is None:
         env = os.environ.copy()

baselines/common/mpi_moments.py

@@ -33,8 +33,8 @@ def mpi_moments(x, axis=0, comm=None, keepdims=False):
 
 def test_runningmeanstd():
     import subprocess
-    subprocess.check_call(['mpirun', '-np', '3', 
-        'python','-c', 
+    subprocess.check_call(['mpirun', '-np', '3',
+        'python','-c',
         'from baselines.common.mpi_moments import _helper_runningmeanstd; _helper_runningmeanstd()'])
 
 def _helper_runningmeanstd():

baselines/common/mpi_util.py

@@ -0,0 +1,101 @@
+from collections import defaultdict
+from mpi4py import MPI
+import os, numpy as np
+import platform
+import shutil
+import subprocess
+
+def sync_from_root(sess, variables, comm=None):
+    """
+    Send the root node's parameters to every worker.
+    Arguments:
+      sess: the TensorFlow session.
+      variables: all parameter variables including optimizer's
+    """
+    if comm is None: comm = MPI.COMM_WORLD
+    rank = comm.Get_rank()
+    for var in variables:
+        if rank == 0:
+            comm.Bcast(sess.run(var))
+        else:
+            import tensorflow as tf
+            returned_var = np.empty(var.shape, dtype='float32')
+            comm.Bcast(returned_var)
+            sess.run(tf.assign(var, returned_var))
+
+def gpu_count():
+    """
+    Count the GPUs on this machine.
+    """
+    if shutil.which('nvidia-smi') is None:
+        return 0
+    output = subprocess.check_output(['nvidia-smi', '--query-gpu=gpu_name', '--format=csv'])
+    return max(0, len(output.split(b'\n')) - 2)
+
+def setup_mpi_gpus():
+    """
+    Set CUDA_VISIBLE_DEVICES using MPI.
+    """
+    num_gpus = gpu_count()
+    if num_gpus == 0:
+        return
+    local_rank, _ = get_local_rank_size(MPI.COMM_WORLD)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(local_rank % num_gpus)
+
+def get_local_rank_size(comm):
+    """
+    Returns the rank of each process on its machine
+    The processes on a given machine will be assigned ranks
+        0, 1, 2, ..., N-1,
+    where N is the number of processes on this machine.
+
+    Useful if you want to assign one gpu per machine
+    """
+    this_node = platform.node()
+    ranks_nodes = comm.allgather((comm.Get_rank(), this_node))
+    node2rankssofar = defaultdict(int)
+    local_rank = None
+    for (rank, node) in ranks_nodes:
+        if rank == comm.Get_rank():
+            local_rank = node2rankssofar[node]
+        node2rankssofar[node] += 1
+    assert local_rank is not None
+    return local_rank, node2rankssofar[this_node]
+
+def share_file(comm, path):
+    """
+    Copies the file from rank 0 to all other ranks
+    Puts it in the same place on all machines
+    """
+    localrank, _ = get_local_rank_size(comm)
+    if comm.Get_rank() == 0:
+        with open(path, 'rb') as fh:
+            data = fh.read()
+        comm.bcast(data)
+    else:
+        data = comm.bcast(None)
+        if localrank == 0:
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            with open(path, 'wb') as fh:
+                fh.write(data)
+    comm.Barrier()
+
+def dict_gather(comm, d, op='mean', assert_all_have_data=True):
+    if comm is None: return d
+    alldicts = comm.allgather(d)
+    size = comm.size
+    k2li = defaultdict(list)
+    for d in alldicts:
+        for (k,v) in d.items():
+            k2li[k].append(v)
+    result = {}
+    for (k,li) in k2li.items():
+        if assert_all_have_data:
+            assert len(li)==size, "only %i out of %i MPI workers have sent '%s'" % (len(li), size, k)
+        if op=='mean':
+            result[k] = np.mean(li, axis=0)
+        elif op=='sum':
+            result[k] = np.sum(li, axis=0)
+        else:
+            assert 0, op
+    return result

baselines/common/policies.py

@@ -0,0 +1,186 @@
+import tensorflow as tf
+from baselines.common import tf_util
+from baselines.a2c.utils import fc
+from baselines.common.distributions import make_pdtype
+from baselines.common.input import observation_placeholder, encode_observation
+from baselines.common.tf_util import adjust_shape
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+from baselines.common.models import get_network_builder
+
+import gym
+
+
+class PolicyWithValue(object):
+    """
+    Encapsulates fields and methods for RL policy and value function estimation with shared parameters
+    """
+
+    def __init__(self, env, observations, latent, estimate_q=False, vf_latent=None, sess=None, **tensors):
+        """
+        Parameters:
+        ----------
+        env             RL environment
+
+        observations    tensorflow placeholder in which the observations will be fed
+
+        latent          latent state from which policy distribution parameters should be inferred
+
+        vf_latent       latent state from which value function should be inferred (if None, then latent is used)
+
+        sess            tensorflow session to run calculations in (if None, default session is used)
+
+        **tensors       tensorflow tensors for additional attributes such as state or mask
+
+        """
+
+        self.X = observations
+        self.state = tf.constant([])
+        self.initial_state = None
+        self.__dict__.update(tensors)
+
+        vf_latent = vf_latent if vf_latent is not None else latent
+
+        vf_latent = tf.layers.flatten(vf_latent)
+        latent = tf.layers.flatten(latent)
+
+        # Based on the action space, will select what probability distribution type
+        self.pdtype = make_pdtype(env.action_space)
+
+        self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01)
+
+        # Take an action
+        self.action = self.pd.sample()
+
+        # Calculate the neg log of our probability
+        self.neglogp = self.pd.neglogp(self.action)
+        self.sess = sess or tf.get_default_session()
+
+        if estimate_q:
+            assert isinstance(env.action_space, gym.spaces.Discrete)
+            self.q = fc(vf_latent, 'q', env.action_space.n)
+            self.vf = self.q
+        else:
+            self.vf = fc(vf_latent, 'vf', 1)
+            self.vf = self.vf[:,0]
+
+    def _evaluate(self, variables, observation, **extra_feed):
+        sess = self.sess
+        feed_dict = {self.X: adjust_shape(self.X, observation)}
+        for inpt_name, data in extra_feed.items():
+            if inpt_name in self.__dict__.keys():
+                inpt = self.__dict__[inpt_name]
+                if isinstance(inpt, tf.Tensor) and inpt._op.type == 'Placeholder':
+                    feed_dict[inpt] = adjust_shape(inpt, data)
+
+        return sess.run(variables, feed_dict)
+
+    def step(self, observation, **extra_feed):
+        """
+        Compute next action(s) given the observation(s)
+
+        Parameters:
+        ----------
+
+        observation     observation data (either single or a batch)
+
+        **extra_feed    additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
+
+        Returns:
+        -------
+        (action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple
+        """
+
+        a, v, state, neglogp = self._evaluate([self.action, self.vf, self.state, self.neglogp], observation, **extra_feed)
+        if state.size == 0:
+            state = None
+        return a, v, state, neglogp
+
+    def value(self, ob, *args, **kwargs):
+        """
+        Compute value estimate(s) given the observation(s)
+
+        Parameters:
+        ----------
+
+        observation     observation data (either single or a batch)
+
+        **extra_feed    additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
+
+        Returns:
+        -------
+        value estimate
+        """
+        return self._evaluate(self.vf, ob, *args, **kwargs)
+
+    def save(self, save_path):
+        tf_util.save_state(save_path, sess=self.sess)
+
+    def load(self, load_path):
+        tf_util.load_state(load_path, sess=self.sess)
+
+def build_policy(env, policy_network, value_network=None,  normalize_observations=False, estimate_q=False, **policy_kwargs):
+    if isinstance(policy_network, str):
+        network_type = policy_network
+        policy_network = get_network_builder(network_type)(**policy_kwargs)
+
+    def policy_fn(nbatch=None, nsteps=None, sess=None, observ_placeholder=None):
+        ob_space = env.observation_space
+
+        X = observ_placeholder if observ_placeholder is not None else observation_placeholder(ob_space, batch_size=nbatch)
+
+        extra_tensors = {}
+
+        if normalize_observations and X.dtype == tf.float32:
+            encoded_x, rms = _normalize_clip_observation(X)
+            extra_tensors['rms'] = rms
+        else:
+            encoded_x = X
+
+        encoded_x = encode_observation(ob_space, encoded_x)
+
+        with tf.variable_scope('pi', reuse=tf.AUTO_REUSE):
+            policy_latent = policy_network(encoded_x)
+            if isinstance(policy_latent, tuple):
+                policy_latent, recurrent_tensors = policy_latent
+
+                if recurrent_tensors is not None:
+                    # recurrent architecture, need a few more steps
+                    nenv = nbatch // nsteps
+                    assert nenv > 0, 'Bad input for recurrent policy: batch size {} smaller than nsteps {}'.format(nbatch, nsteps)
+                    policy_latent, recurrent_tensors = policy_network(encoded_x, nenv)
+                    extra_tensors.update(recurrent_tensors)
+
+
+        _v_net = value_network
+
+        if _v_net is None or _v_net == 'shared':
+            vf_latent = policy_latent
+        else:
+            if _v_net == 'copy':
+                _v_net = policy_network
+            else:
+                assert callable(_v_net)
+
+            with tf.variable_scope('vf', reuse=tf.AUTO_REUSE):
+                # TODO recurrent architectures are not supported with value_network=copy yet
+                vf_latent = _v_net(encoded_x)
+
+        policy = PolicyWithValue(
+            env=env,
+            observations=X,
+            latent=policy_latent,
+            vf_latent=vf_latent,
+            sess=sess,
+            estimate_q=estimate_q,
+            **extra_tensors
+        )
+        return policy
+
+    return policy_fn
+
+
+def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]):
+    rms = RunningMeanStd(shape=x.shape[1:])
+    norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range))
+    return norm_x, rms
+

baselines/common/retro_wrappers.py

@@ -0,0 +1,293 @@
+ # flake8: noqa F403, F405
+from .atari_wrappers import *
+import numpy as np
+import gym
+
+class TimeLimit(gym.Wrapper):
+    def __init__(self, env, max_episode_steps=None):
+        super(TimeLimit, self).__init__(env)
+        self._max_episode_steps = max_episode_steps
+        self._elapsed_steps = 0
+
+    def step(self, ac):
+        observation, reward, done, info = self.env.step(ac)
+        self._elapsed_steps += 1
+        if self._elapsed_steps >= self._max_episode_steps:
+            done = True
+            info['TimeLimit.truncated'] = True
+        return observation, reward, done, info
+
+    def reset(self, **kwargs):
+        self._elapsed_steps = 0
+        return self.env.reset(**kwargs)
+
+class StochasticFrameSkip(gym.Wrapper):
+    def __init__(self, env, n, stickprob):
+        gym.Wrapper.__init__(self, env)
+        self.n = n
+        self.stickprob = stickprob
+        self.curac = None
+        self.rng = np.random.RandomState()
+        self.supports_want_render = hasattr(env, "supports_want_render")
+
+    def reset(self, **kwargs):
+        self.curac = None
+        return self.env.reset(**kwargs)
+
+    def step(self, ac):
+        done = False
+        totrew = 0
+        for i in range(self.n):
+            # First step after reset, use action
+            if self.curac is None:
+                self.curac = ac
+            # First substep, delay with probability=stickprob
+            elif i==0:
+                if self.rng.rand() > self.stickprob:
+                    self.curac = ac
+            # Second substep, new action definitely kicks in
+            elif i==1:
+                self.curac = ac
+            if self.supports_want_render and i<self.n-1:
+                ob, rew, done, info = self.env.step(self.curac, want_render=False)
+            else:
+                ob, rew, done, info = self.env.step(self.curac)
+            totrew += rew
+            if done: break
+        return ob, totrew, done, info
+
+    def seed(self, s):
+        self.rng.seed(s)
+
+class PartialFrameStack(gym.Wrapper):
+    def __init__(self, env, k, channel=1):
+        """
+        Stack one channel (channel keyword) from previous frames
+        """
+        gym.Wrapper.__init__(self, env)
+        shp = env.observation_space.shape
+        self.channel = channel
+        self.observation_space = gym.spaces.Box(low=0, high=255,
+            shape=(shp[0], shp[1], shp[2] + k - 1),
+            dtype=env.observation_space.dtype)
+        self.k = k
+        self.frames = deque([], maxlen=k)
+        shp = env.observation_space.shape
+
+    def reset(self):
+        ob = self.env.reset()
+        assert ob.shape[2] > self.channel
+        for _ in range(self.k):
+            self.frames.append(ob)
+        return self._get_ob()
+
+    def step(self, ac):
+        ob, reward, done, info = self.env.step(ac)
+        self.frames.append(ob)
+        return self._get_ob(), reward, done, info
+
+    def _get_ob(self):
+        assert len(self.frames) == self.k
+        return np.concatenate([frame if i==self.k-1 else frame[:,:,self.channel:self.channel+1]
+            for (i, frame) in enumerate(self.frames)], axis=2)
+
+class Downsample(gym.ObservationWrapper):
+    def __init__(self, env, ratio):
+        """
+        Downsample images by a factor of ratio
+        """
+        gym.ObservationWrapper.__init__(self, env)
+        (oldh, oldw, oldc) = env.observation_space.shape
+        newshape = (oldh//ratio, oldw//ratio, oldc)
+        self.observation_space = spaces.Box(low=0, high=255,
+            shape=newshape, dtype=np.uint8)
+
+    def observation(self, frame):
+        height, width, _ = self.observation_space.shape
+        frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA)
+        if frame.ndim == 2:
+            frame = frame[:,:,None]
+        return frame
+
+class Rgb2gray(gym.ObservationWrapper):
+    def __init__(self, env):
+        """
+        Downsample images by a factor of ratio
+        """
+        gym.ObservationWrapper.__init__(self, env)
+        (oldh, oldw, _oldc) = env.observation_space.shape
+        self.observation_space = spaces.Box(low=0, high=255,
+            shape=(oldh, oldw, 1), dtype=np.uint8)
+
+    def observation(self, frame):
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        return frame[:,:,None]
+
+
+class MovieRecord(gym.Wrapper):
+    def __init__(self, env, savedir, k):
+        gym.Wrapper.__init__(self, env)
+        self.savedir = savedir
+        self.k = k
+        self.epcount = 0
+    def reset(self):
+        if self.epcount % self.k == 0:
+            print('saving movie this episode', self.savedir)
+            self.env.unwrapped.movie_path = self.savedir
+        else:
+            print('not saving this episode')
+            self.env.unwrapped.movie_path = None
+            self.env.unwrapped.movie = None
+        self.epcount += 1
+        return self.env.reset()
+
+class AppendTimeout(gym.Wrapper):
+    def __init__(self, env):
+        gym.Wrapper.__init__(self, env)
+        self.action_space = env.action_space
+        self.timeout_space = gym.spaces.Box(low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32)
+        self.original_os = env.observation_space
+        if isinstance(self.original_os, gym.spaces.Dict):
+            import copy
+            ordered_dict = copy.deepcopy(self.original_os.spaces)
+            ordered_dict['value_estimation_timeout'] = self.timeout_space
+            self.observation_space = gym.spaces.Dict(ordered_dict)
+            self.dict_mode = True
+        else:
+            self.observation_space = gym.spaces.Dict({
+                'original': self.original_os,
+                'value_estimation_timeout': self.timeout_space
+                })
+            self.dict_mode = False
+        self.ac_count = None
+        while 1:
+            if not hasattr(env, "_max_episode_steps"):  # Looking for TimeLimit wrapper that has this field
+                env = env.env
+                continue
+            break
+        self.timeout = env._max_episode_steps
+
+    def step(self, ac):
+        self.ac_count += 1
+        ob, rew, done, info = self.env.step(ac)
+        return self._process(ob), rew, done, info
+
+    def reset(self):
+        self.ac_count = 0
+        return self._process(self.env.reset())
+
+    def _process(self, ob):
+        fracmissing = 1 - self.ac_count / self.timeout
+        if self.dict_mode:
+            ob['value_estimation_timeout'] = fracmissing
+        else:
+            return { 'original': ob, 'value_estimation_timeout': fracmissing }
+
+class StartDoingRandomActionsWrapper(gym.Wrapper):
+    """
+    Warning: can eat info dicts, not good if you depend on them
+    """
+    def __init__(self, env, max_random_steps, on_startup=True, every_episode=False):
+        gym.Wrapper.__init__(self, env)
+        self.on_startup = on_startup
+        self.every_episode = every_episode
+        self.random_steps = max_random_steps
+        self.last_obs = None
+        if on_startup:
+            self.some_random_steps()
+
+    def some_random_steps(self):
+        self.last_obs = self.env.reset()
+        n = np.random.randint(self.random_steps)
+        #print("running for random %i frames" % n)
+        for _ in range(n):
+            self.last_obs, _, done, _ = self.env.step(self.env.action_space.sample())
+            if done: self.last_obs = self.env.reset()
+
+    def reset(self):
+        return self.last_obs
+
+    def step(self, a):
+        self.last_obs, rew, done, info = self.env.step(a)
+        if done:
+            self.last_obs = self.env.reset()
+            if self.every_episode:
+                self.some_random_steps()
+        return self.last_obs, rew, done, info
+
+def make_retro(*, game, state, max_episode_steps, **kwargs):
+    import retro
+    env = retro.make(game, state, **kwargs)
+    env = StochasticFrameSkip(env, n=4, stickprob=0.25)
+    if max_episode_steps is not None:
+        env = TimeLimit(env, max_episode_steps=max_episode_steps)
+    return env
+
+def wrap_deepmind_retro(env, scale=True, frame_stack=4):
+    """
+    Configure environment for retro games, using config similar to DeepMind-style Atari in wrap_deepmind
+    """
+    env = WarpFrame(env)
+    env = ClipRewardEnv(env)
+    env = FrameStack(env, frame_stack)
+    if scale:
+        env = ScaledFloatFrame(env)
+    return env
+
+class SonicDiscretizer(gym.ActionWrapper):
+    """
+    Wrap a gym-retro environment and make it use discrete
+    actions for the Sonic game.
+    """
+    def __init__(self, env):
+        super(SonicDiscretizer, self).__init__(env)
+        buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", "RIGHT", "C", "Y", "X", "Z"]
+        actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'], ['DOWN'],
+                   ['DOWN', 'B'], ['B']]
+        self._actions = []
+        for action in actions:
+            arr = np.array([False] * 12)
+            for button in action:
+                arr[buttons.index(button)] = True
+            self._actions.append(arr)
+        self.action_space = gym.spaces.Discrete(len(self._actions))
+
+    def action(self, a): # pylint: disable=W0221
+        return self._actions[a].copy()
+
+class RewardScaler(gym.RewardWrapper):
+    """
+    Bring rewards to a reasonable scale for PPO.
+    This is incredibly important and effects performance
+    drastically.
+    """
+    def __init__(self, env, scale=0.01):
+        super(RewardScaler, self).__init__(env)
+        self.scale = scale
+
+    def reward(self, reward):
+        return reward * self.scale
+
+class AllowBacktracking(gym.Wrapper):
+    """
+    Use deltas in max(X) as the reward, rather than deltas
+    in X. This way, agents are not discouraged too heavily
+    from exploring backwards if there is no way to advance
+    head-on in the level.
+    """
+    def __init__(self, env):
+        super(AllowBacktracking, self).__init__(env)
+        self._cur_x = 0
+        self._max_x = 0
+
+    def reset(self, **kwargs): # pylint: disable=E0202
+        self._cur_x = 0
+        self._max_x = 0
+        return self.env.reset(**kwargs)
+
+    def step(self, action): # pylint: disable=E0202
+        obs, rew, done, info = self.env.step(action)
+        self._cur_x += rew
+        rew = max(0, self._cur_x - self._max_x)
+        self._max_x = max(self._max_x, self._cur_x)
+        return obs, rew, done, info

baselines/common/runners.py

@@ -5,7 +5,7 @@ class AbstractEnvRunner(ABC):
     def __init__(self, *, env, model, nsteps):
         self.env = env
         self.model = model
-        nenv = env.num_envs
+        self.nenv = nenv = env.num_envs if hasattr(env, 'num_envs') else 1
         self.batch_ob_shape = (nenv*nsteps,) + env.observation_space.shape
         self.obs = np.zeros((nenv,) + env.observation_space.shape, dtype=env.observation_space.dtype.name)
         self.obs[:] = env.reset()
@@ -16,3 +16,4 @@ class AbstractEnvRunner(ABC):
     @abstractmethod
     def run(self):
         raise NotImplementedError
+

baselines/common/running_mean_std.py

@@ -1,4 +1,7 @@
+import tensorflow as tf
 import numpy as np
+from baselines.common.tf_util import get_session
+
 class RunningMeanStd(object):
     # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
     def __init__(self, epsilon=1e-4, shape=()):
@@ -13,20 +16,71 @@ class RunningMeanStd(object):
         self.update_from_moments(batch_mean, batch_var, batch_count)
 
     def update_from_moments(self, batch_mean, batch_var, batch_count):
-        delta = batch_mean - self.mean
-        tot_count = self.count + batch_count
+        self.mean, self.var, self.count = update_mean_var_count_from_moments(
+            self.mean, self.var, self.count, batch_mean, batch_var, batch_count)
 
-        new_mean = self.mean + delta * batch_count / tot_count        
-        m_a = self.var * (self.count)
-        m_b = batch_var * (batch_count)
-        M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
-        new_var = M2 / (self.count + batch_count)
+def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count):
+    delta = batch_mean - mean
+    tot_count = count + batch_count
+
+    new_mean = mean + delta * batch_count / tot_count
+    m_a = var * count
+    m_b = batch_var * batch_count
+    M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count
+    new_var = M2 / tot_count
+    new_count = tot_count
+
+    return new_mean, new_var, new_count
+
+
+class TfRunningMeanStd(object):
+    # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+    '''
+    TensorFlow variables-based implmentation of computing running mean and std
+    Benefit of this implementation is that it can be saved / loaded together with the tensorflow model
+    '''
+    def __init__(self, epsilon=1e-4, shape=(), scope=''):
+        sess = get_session()
+
+        self._new_mean = tf.placeholder(shape=shape, dtype=tf.float64)
+        self._new_var = tf.placeholder(shape=shape, dtype=tf.float64)
+        self._new_count = tf.placeholder(shape=(), dtype=tf.float64)
+
+
+        with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
+            self._mean  = tf.get_variable('mean',  initializer=np.zeros(shape, 'float64'),      dtype=tf.float64)
+            self._var   = tf.get_variable('std',   initializer=np.ones(shape, 'float64'),       dtype=tf.float64)
+            self._count = tf.get_variable('count', initializer=np.full((), epsilon, 'float64'), dtype=tf.float64)
+
+        self.update_ops = tf.group([
+            self._var.assign(self._new_var),
+            self._mean.assign(self._new_mean),
+            self._count.assign(self._new_count)
+        ])
+
+        sess.run(tf.variables_initializer([self._mean, self._var, self._count]))
+        self.sess = sess
+        self._set_mean_var_count()
+
+    def _set_mean_var_count(self):
+        self.mean, self.var, self.count = self.sess.run([self._mean, self._var, self._count])
+
+    def update(self, x):
+        batch_mean = np.mean(x, axis=0)
+        batch_var = np.var(x, axis=0)
+        batch_count = x.shape[0]
+
+        new_mean, new_var, new_count = update_mean_var_count_from_moments(self.mean, self.var, self.count, batch_mean, batch_var, batch_count)
+
+        self.sess.run(self.update_ops, feed_dict={
+            self._new_mean: new_mean,
+            self._new_var: new_var,
+            self._new_count: new_count
+        })
+
+        self._set_mean_var_count()
 
-        new_count = batch_count + self.count
 
-        self.mean = new_mean
-        self.var = new_var
-        self.count = new_count    
 
 def test_runningmeanstd():
     for (x1, x2, x3) in [
@@ -43,4 +97,91 @@ def test_runningmeanstd():
         rms.update(x3)
         ms2 = [rms.mean, rms.var]
 
-        assert np.allclose(ms1, ms2)
+        np.testing.assert_allclose(ms1, ms2)
+
+def test_tf_runningmeanstd():
+    for (x1, x2, x3) in [
+        (np.random.randn(3), np.random.randn(4), np.random.randn(5)),
+        (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)),
+        ]:
+
+        rms = TfRunningMeanStd(epsilon=0.0, shape=x1.shape[1:], scope='running_mean_std' + str(np.random.randint(0, 128)))
+
+        x = np.concatenate([x1, x2, x3], axis=0)
+        ms1 = [x.mean(axis=0), x.var(axis=0)]
+        rms.update(x1)
+        rms.update(x2)
+        rms.update(x3)
+        ms2 = [rms.mean, rms.var]
+
+        np.testing.assert_allclose(ms1, ms2)
+
+
+def profile_tf_runningmeanstd():
+    import time
+    from baselines.common import tf_util
+
+    tf_util.get_session( config=tf.ConfigProto(
+        inter_op_parallelism_threads=1,
+        intra_op_parallelism_threads=1,
+        allow_soft_placement=True
+    ))
+
+    x = np.random.random((376,))
+
+    n_trials = 10000
+    rms = RunningMeanStd()
+    tfrms = TfRunningMeanStd()
+
+    tic1 = time.time()
+    for _ in range(n_trials):
+        rms.update(x)
+
+    tic2 = time.time()
+    for _ in range(n_trials):
+        tfrms.update(x)
+
+    tic3 = time.time()
+
+    print('rms update time ({} trials): {} s'.format(n_trials, tic2 - tic1))
+    print('tfrms update time ({} trials): {} s'.format(n_trials, tic3 - tic2))
+
+
+    tic1 = time.time()
+    for _ in range(n_trials):
+        z1 = rms.mean
+
+    tic2 = time.time()
+    for _ in range(n_trials):
+        z2 = tfrms.mean
+
+    assert z1 == z2
+
+    tic3 = time.time()
+
+    print('rms get mean time ({} trials): {} s'.format(n_trials, tic2 - tic1))
+    print('tfrms get mean time ({} trials): {} s'.format(n_trials, tic3 - tic2))
+
+
+
+    '''
+    options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) #pylint: disable=E1101
+    run_metadata = tf.RunMetadata()
+    profile_opts = dict(options=options, run_metadata=run_metadata)
+
+
+
+    from tensorflow.python.client import timeline
+    fetched_timeline = timeline.Timeline(run_metadata.step_stats) #pylint: disable=E1101
+    chrome_trace = fetched_timeline.generate_chrome_trace_format()
+    outfile = '/tmp/timeline.json'
+    with open(outfile, 'wt') as f:
+        f.write(chrome_trace)
+    print(f'Successfully saved profile to {outfile}. Exiting.')
+    exit(0)
+    '''
+
+
+
+if __name__ == '__main__':
+   profile_tf_runningmeanstd()

baselines/common/running_stat.py

@@ -1,46 +0,0 @@
-import numpy as np
-
-# http://www.johndcook.com/blog/standard_deviation/
-class RunningStat(object):
-    def __init__(self, shape):
-        self._n = 0
-        self._M = np.zeros(shape)
-        self._S = np.zeros(shape)
-    def push(self, x):
-        x = np.asarray(x)
-        assert x.shape == self._M.shape
-        self._n += 1
-        if self._n == 1:
-            self._M[...] = x
-        else:
-            oldM = self._M.copy()
-            self._M[...] = oldM + (x - oldM)/self._n
-            self._S[...] = self._S + (x - oldM)*(x - self._M)
-    @property
-    def n(self):
-        return self._n
-    @property
-    def mean(self):
-        return self._M
-    @property
-    def var(self):
-        return self._S/(self._n - 1) if self._n > 1 else np.square(self._M)
-    @property
-    def std(self):
-        return np.sqrt(self.var)
-    @property
-    def shape(self):
-        return self._M.shape
-
-def test_running_stat():
-    for shp in ((), (3,), (3,4)):
-        li = []
-        rs = RunningStat(shp)
-        for _ in range(5):
-            val = np.random.randn(*shp)
-            rs.push(val)
-            li.append(val)
-            m = np.mean(li, axis=0)
-            assert np.allclose(rs.mean, m)
-            v = np.square(m) if (len(li) == 1) else np.var(li, ddof=1, axis=0)
-            assert np.allclose(rs.var, v)

baselines/common/test_identity.py

@@ -1,44 +0,0 @@
-import pytest
-import tensorflow as tf
-import random
-import numpy as np
-from gym.spaces import np_random
-
-from baselines.a2c import a2c
-from baselines.ppo2 import ppo2
-from baselines.common.identity_env import IdentityEnv
-from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-from baselines.ppo2.policies import MlpPolicy
-
-
-learn_func_list = [
-    lambda e: a2c.learn(policy=MlpPolicy, env=e, seed=0, total_timesteps=50000),
-    lambda e: ppo2.learn(policy=MlpPolicy, env=e, total_timesteps=50000, lr=1e-3, nsteps=128, ent_coef=0.01)
-]
-
-
-@pytest.mark.slow
-@pytest.mark.parametrize("learn_func", learn_func_list)
-def test_identity(learn_func):
-    '''
-    Test if the algorithm (with a given policy) 
-    can learn an identity transformation (i.e. return observation as an action)
-    '''
-    np.random.seed(0)
-    np_random.seed(0)
-    random.seed(0)
-
-    env = DummyVecEnv([lambda: IdentityEnv(10)])
-
-    with tf.Graph().as_default(), tf.Session().as_default():
-        tf.set_random_seed(0)
-        model = learn_func(env)
-
-        N_TRIALS = 1000
-        sum_rew = 0
-        obs = env.reset()
-        for i in range(N_TRIALS):
-            obs, rew, done, _ = env.step(model.step(obs)[0])
-            sum_rew += rew
-
-        assert sum_rew > 0.9 * N_TRIALS

baselines/common/tests/envs/fixed_sequence_env.py

@@ -0,0 +1,44 @@
+import numpy as np
+from gym import Env
+from gym.spaces import Discrete
+
+
+class FixedSequenceEnv(Env):
+    def __init__(
+            self,
+            n_actions=10,
+            seed=0,
+            episode_len=100
+    ):
+        self.np_random = np.random.RandomState()
+        self.np_random.seed(seed)
+        self.sequence = [self.np_random.randint(0, n_actions-1) for _ in range(episode_len)]
+
+        self.action_space = Discrete(n_actions)
+        self.observation_space = Discrete(1)
+
+        self.episode_len = episode_len
+        self.time = 0
+        self.reset()
+
+    def reset(self):
+        self.time = 0
+        return 0
+
+    def step(self, actions):
+        rew = self._get_reward(actions)
+        self._choose_next_state()
+        done = False
+        if self.episode_len and self.time >= self.episode_len:
+            rew = 0
+            done = True
+
+        return 0, rew, done, {}
+
+    def _choose_next_state(self):
+        self.time += 1
+
+    def _get_reward(self, actions):
+        return 1 if actions == self.sequence[self.time] else 0
+
+

baselines/common/tests/envs/identity_env.py

@@ -0,0 +1,70 @@
+import numpy as np
+from abc import abstractmethod
+from gym import Env
+from gym.spaces import Discrete, Box
+
+
+class IdentityEnv(Env):
+    def __init__(
+            self,
+            episode_len=None
+    ):
+
+        self.episode_len = episode_len
+        self.time = 0
+        self.reset()
+
+    def reset(self):
+        self._choose_next_state()
+        self.time = 0
+        self.observation_space = self.action_space
+
+        return self.state
+
+    def step(self, actions):
+        rew = self._get_reward(actions)
+        self._choose_next_state()
+        done = False
+        if self.episode_len and self.time >= self.episode_len:
+            rew = 0
+            done = True
+
+        return self.state, rew, done, {}
+
+    def _choose_next_state(self):
+        self.state = self.action_space.sample()
+        self.time += 1
+
+    @abstractmethod
+    def _get_reward(self, actions):
+        raise NotImplementedError
+
+
+class DiscreteIdentityEnv(IdentityEnv):
+    def __init__(
+            self,
+            dim,
+            episode_len=None,
+    ):
+
+        self.action_space = Discrete(dim)
+        super().__init__(episode_len=episode_len)
+
+    def _get_reward(self, actions):
+        return 1 if self.state == actions else 0
+
+
+class BoxIdentityEnv(IdentityEnv):
+    def __init__(
+            self,
+            shape,
+            episode_len=None,
+    ):
+
+        self.action_space = Box(low=-1.0, high=1.0, shape=shape)
+        super().__init__(episode_len=episode_len)
+
+    def _get_reward(self, actions):
+        diff = actions - self.state
+        diff = diff[:]
+        return -0.5 * np.dot(diff, diff)

baselines/common/tests/envs/mnist_env.py

@@ -0,0 +1,70 @@
+import os.path as osp
+import numpy as np
+import tempfile
+from gym import Env
+from gym.spaces import Discrete, Box
+
+
+
+class MnistEnv(Env):
+    def __init__(
+            self,
+            seed=0,
+            episode_len=None,
+            no_images=None
+    ):
+        import filelock
+        from tensorflow.examples.tutorials.mnist import input_data
+        # we could use temporary directory for this with a context manager and
+        # TemporaryDirecotry, but then each test that uses mnist would re-download the data
+        # this way the data is not cleaned up, but we only download it once per machine
+        mnist_path = osp.join(tempfile.gettempdir(), 'MNIST_data')
+        with filelock.FileLock(mnist_path + '.lock'):
+           self.mnist = input_data.read_data_sets(mnist_path)
+
+        self.np_random = np.random.RandomState()
+        self.np_random.seed(seed)
+
+        self.observation_space = Box(low=0.0, high=1.0, shape=(28,28,1))
+        self.action_space = Discrete(10)
+        self.episode_len = episode_len
+        self.time = 0
+        self.no_images = no_images
+
+        self.train_mode()
+        self.reset()
+
+    def reset(self):
+        self._choose_next_state()
+        self.time = 0
+
+        return self.state[0]
+
+    def step(self, actions):
+        rew = self._get_reward(actions)
+        self._choose_next_state()
+        done = False
+        if self.episode_len and self.time >= self.episode_len:
+            rew = 0
+            done = True
+
+        return self.state[0], rew, done, {}
+
+    def train_mode(self):
+        self.dataset = self.mnist.train
+
+    def test_mode(self):
+        self.dataset = self.mnist.test
+
+    def _choose_next_state(self):
+        max_index = (self.no_images if self.no_images is not None else self.dataset.num_examples) - 1
+        index = self.np_random.randint(0, max_index)
+        image = self.dataset.images[index].reshape(28,28,1)*255
+        label = self.dataset.labels[index]
+        self.state = (image, label)
+        self.time += 1
+
+    def _get_reward(self, actions):
+        return 1 if self.state[1] == actions else 0
+
+

baselines/common/tests/test_cartpole.py

@@ -0,0 +1,44 @@
+import pytest
+import gym
+
+from baselines.run import get_learn_function
+from baselines.common.tests.util import reward_per_episode_test
+
+common_kwargs = dict(
+    total_timesteps=30000,
+    network='mlp',
+    gamma=1.0,
+    seed=0,
+)
+
+learn_kwargs = {
+    'a2c' : dict(nsteps=32, value_network='copy', lr=0.05),
+    'acer': dict(value_network='copy'),
+    'acktr': dict(nsteps=32, value_network='copy', is_async=False),
+    'deepq': dict(total_timesteps=20000),
+    'ppo2': dict(value_network='copy'),
+    'trpo_mpi': {}
+}
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", learn_kwargs.keys())
+def test_cartpole(alg):
+    '''
+    Test if the algorithm (with an mlp policy)
+    can learn to balance the cartpole
+    '''
+
+    kwargs = common_kwargs.copy()
+    kwargs.update(learn_kwargs[alg])
+
+    learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
+    def env_fn():
+
+        env = gym.make('CartPole-v0')
+        env.seed(0)
+        return env
+
+    reward_per_episode_test(env_fn, learn_fn, 100)
+
+if __name__ == '__main__':
+    test_cartpole('acer')

baselines/common/tests/test_doc_examples.py

@@ -0,0 +1,48 @@
+import pytest
+try:
+    import mujoco_py
+    _mujoco_present = True
+except BaseException:
+    mujoco_py = None
+    _mujoco_present = False
+
+
+@pytest.mark.skipif(
+    not _mujoco_present,
+    reason='error loading mujoco - either mujoco / mujoco key not present, or LD_LIBRARY_PATH is not pointing to mujoco library'
+)
+def test_lstm_example():
+    import tensorflow as tf
+    from baselines.common import policies, models, cmd_util
+    from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+
+    # create vectorized environment
+    venv = DummyVecEnv([lambda: cmd_util.make_mujoco_env('Reacher-v2', seed=0)])
+
+    with tf.Session() as sess:
+        # build policy based on lstm network with 128 units
+        policy = policies.build_policy(venv, models.lstm(128))(nbatch=1, nsteps=1)
+
+        # initialize tensorflow variables
+        sess.run(tf.global_variables_initializer())
+
+        # prepare environment variables
+        ob = venv.reset()
+        state = policy.initial_state
+        done = [False]
+        step_counter = 0
+
+        # run a single episode until the end (i.e. until done)
+        while True:
+            action, _, state, _ = policy.step(ob, S=state, M=done)
+            ob, reward, done, _ = venv.step(action)
+            step_counter += 1
+            if done:
+                break
+
+
+        assert step_counter > 5
+
+
+
+

baselines/common/tests/test_env_after_learn.py

@@ -0,0 +1,27 @@
+import pytest
+import gym
+import tensorflow as tf
+
+from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
+from baselines.run import get_learn_function
+from baselines.common.tf_util import make_session
+
+algos = ['a2c', 'acer', 'acktr', 'deepq', 'ppo2', 'trpo_mpi']
+
+@pytest.mark.parametrize('algo', algos)
+def test_env_after_learn(algo):
+    def make_env():
+        # acktr requires too much RAM, fails on travis
+        env = gym.make('CartPole-v1' if algo == 'acktr' else 'PongNoFrameskip-v4')
+        return env
+
+    make_session(make_default=True, graph=tf.Graph())
+    env = SubprocVecEnv([make_env])
+
+    learn = get_learn_function(algo)
+
+    # Commenting out the following line resolves the issue, though crash happens at env.reset().
+    learn(network='mlp', env=env, total_timesteps=0, load_path=None, seed=None)
+
+    env.reset()
+    env.close()

baselines/common/tests/test_fixed_sequence.py

@@ -0,0 +1,51 @@
+import pytest
+from baselines.common.tests.envs.fixed_sequence_env import FixedSequenceEnv
+
+from baselines.common.tests.util import simple_test
+from baselines.run import get_learn_function
+
+common_kwargs = dict(
+    seed=0,
+    total_timesteps=50000,
+)
+
+learn_kwargs = {
+    'a2c': {},
+    'ppo2': dict(nsteps=10, ent_coef=0.0, nminibatches=1),
+    # TODO enable sequential models for trpo_mpi (proper handling of nbatch and nsteps)
+    # github issue: https://github.com/openai/baselines/issues/188
+    # 'trpo_mpi': lambda e, p: trpo_mpi.learn(policy_fn=p(env=e), env=e, max_timesteps=30000, timesteps_per_batch=100, cg_iters=10, gamma=0.9, lam=1.0, max_kl=0.001)
+}
+
+
+alg_list = learn_kwargs.keys()
+rnn_list = ['lstm']
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", alg_list)
+@pytest.mark.parametrize("rnn", rnn_list)
+def test_fixed_sequence(alg, rnn):
+    '''
+    Test if the algorithm (with a given policy)
+    can learn an identity transformation (i.e. return observation as an action)
+    '''
+
+    kwargs = learn_kwargs[alg]
+    kwargs.update(common_kwargs)
+
+    episode_len = 5
+    env_fn = lambda: FixedSequenceEnv(10, episode_len=episode_len)
+    learn = lambda e: get_learn_function(alg)(
+        env=e,
+        network=rnn,
+        **kwargs
+    )
+
+    simple_test(env_fn, learn, 0.7)
+
+
+if __name__ == '__main__':
+    test_fixed_sequence('ppo2', 'lstm')
+
+
+

baselines/common/tests/test_identity.py

@@ -0,0 +1,59 @@
+import pytest
+from baselines.common.tests.envs.identity_env import DiscreteIdentityEnv, BoxIdentityEnv
+from baselines.run import get_learn_function
+from baselines.common.tests.util import simple_test
+
+common_kwargs = dict(
+    total_timesteps=30000,
+    network='mlp',
+    gamma=0.9,
+    seed=0,
+)
+
+learn_kwargs = {
+    'a2c' : {},
+    'acktr': {},
+    'deepq': {},
+    'ddpg': dict(layer_norm=True),
+    'ppo2': dict(lr=1e-3, nsteps=64, ent_coef=0.0),
+    'trpo_mpi': dict(timesteps_per_batch=100, cg_iters=10, gamma=0.9, lam=1.0, max_kl=0.01)
+}
+
+
+algos_disc = ['a2c', 'acktr', 'deepq', 'ppo2', 'trpo_mpi']
+algos_cont = ['a2c', 'acktr', 'ddpg',  'ppo2', 'trpo_mpi']
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", algos_disc)
+def test_discrete_identity(alg):
+    '''
+    Test if the algorithm (with an mlp policy)
+    can learn an identity transformation (i.e. return observation as an action)
+    '''
+
+    kwargs = learn_kwargs[alg]
+    kwargs.update(common_kwargs)
+
+    learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
+    env_fn = lambda: DiscreteIdentityEnv(10, episode_len=100)
+    simple_test(env_fn, learn_fn, 0.9)
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", algos_cont)
+def test_continuous_identity(alg):
+    '''
+    Test if the algorithm (with an mlp policy)
+    can learn an identity transformation (i.e. return observation as an action)
+    to a required precision
+    '''
+
+    kwargs = learn_kwargs[alg]
+    kwargs.update(common_kwargs)
+    learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
+
+    env_fn = lambda: BoxIdentityEnv((1,), episode_len=100)
+    simple_test(env_fn, learn_fn, -0.1)
+
+if __name__ == '__main__':
+    test_continuous_identity('ddpg')
+

baselines/common/tests/test_mnist.py

@@ -0,0 +1,49 @@
+import pytest
+
+# from baselines.acer import acer_simple as acer
+from baselines.common.tests.envs.mnist_env import MnistEnv
+from baselines.common.tests.util import simple_test
+from baselines.run import get_learn_function
+
+
+# TODO investigate a2c and ppo2 failures - is it due to bad hyperparameters for this problem?
+# GitHub issue https://github.com/openai/baselines/issues/189
+common_kwargs = {
+    'seed': 0,
+    'network':'cnn',
+    'gamma':0.9,
+    'pad':'SAME'
+}
+
+learn_args = {
+    'a2c': dict(total_timesteps=50000),
+    'acer': dict(total_timesteps=20000),
+    'deepq': dict(total_timesteps=5000),
+    'acktr': dict(total_timesteps=30000),
+    'ppo2': dict(total_timesteps=50000, lr=1e-3, nsteps=128, ent_coef=0.0),
+    'trpo_mpi': dict(total_timesteps=80000, timesteps_per_batch=100, cg_iters=10, lam=1.0, max_kl=0.001)
+}
+
+
+#tests pass, but are too slow on travis. Same algorithms are covered
+# by other tests with less compute-hungry nn's and by benchmarks
+@pytest.mark.skip
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", learn_args.keys())
+def test_mnist(alg):
+    '''
+    Test if the algorithm can learn to classify MNIST digits.
+    Uses CNN policy.
+    '''
+
+    learn_kwargs = learn_args[alg]
+    learn_kwargs.update(common_kwargs)
+
+    learn = get_learn_function(alg)
+    learn_fn = lambda e: learn(env=e, **learn_kwargs)
+    env_fn = lambda: MnistEnv(seed=0, episode_len=100)
+
+    simple_test(env_fn, learn_fn, 0.6)
+
+if __name__ == '__main__':
+    test_mnist('acer')

baselines/common/tests/test_serialization.py

@@ -0,0 +1,134 @@
+import os
+import gym
+import tempfile
+import pytest
+import tensorflow as tf
+import numpy as np
+
+from baselines.common.tests.envs.mnist_env import MnistEnv
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.run import get_learn_function
+from baselines.common.tf_util import make_session, get_session
+
+from functools import partial
+
+
+learn_kwargs = {
+    'deepq': {},
+    'a2c': {},
+    'acktr': {},
+    'acer': {},
+    'ppo2': {'nminibatches': 1, 'nsteps': 10},
+    'trpo_mpi': {},
+}
+
+network_kwargs = {
+    'mlp': {},
+    'cnn': {'pad': 'SAME'},
+    'lstm': {},
+    'cnn_lnlstm': {'pad': 'SAME'}
+}
+
+
+@pytest.mark.parametrize("learn_fn", learn_kwargs.keys())
+@pytest.mark.parametrize("network_fn", network_kwargs.keys())
+def test_serialization(learn_fn, network_fn):
+    '''
+    Test if the trained model can be serialized
+    '''
+
+
+    if network_fn.endswith('lstm') and learn_fn in ['acer', 'acktr', 'trpo_mpi', 'deepq']:
+            # TODO make acktr work with recurrent policies
+            # and test
+            # github issue: https://github.com/openai/baselines/issues/660
+            return
+
+    env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)])
+    ob = env.reset().copy()
+    learn = get_learn_function(learn_fn)
+
+    kwargs = {}
+    kwargs.update(network_kwargs[network_fn])
+    kwargs.update(learn_kwargs[learn_fn])
+
+
+    learn = partial(learn, env=env, network=network_fn, seed=0, **kwargs)
+
+    with tempfile.TemporaryDirectory() as td:
+        model_path = os.path.join(td, 'serialization_test_model')
+
+        with tf.Graph().as_default(), make_session().as_default():
+            model = learn(total_timesteps=100)
+            model.save(model_path)
+            mean1, std1 = _get_action_stats(model, ob)
+            variables_dict1 = _serialize_variables()
+
+        with tf.Graph().as_default(), make_session().as_default():
+            model = learn(total_timesteps=0, load_path=model_path)
+            mean2, std2 = _get_action_stats(model, ob)
+            variables_dict2 = _serialize_variables()
+
+        for k, v in variables_dict1.items():
+            np.testing.assert_allclose(v, variables_dict2[k], atol=0.01,
+                err_msg='saved and loaded variable {} value mismatch'.format(k))
+
+        np.testing.assert_allclose(mean1, mean2, atol=0.5)
+        np.testing.assert_allclose(std1, std2, atol=0.5)
+
+
+@pytest.mark.parametrize("learn_fn", learn_kwargs.keys())
+@pytest.mark.parametrize("network_fn", ['mlp'])
+def test_coexistence(learn_fn, network_fn):
+    '''
+    Test if more than one model can exist at a time
+    '''
+
+    if learn_fn == 'deepq':
+            # TODO enable multiple DQN models to be useable at the same time
+            # github issue https://github.com/openai/baselines/issues/656
+            return
+
+    if network_fn.endswith('lstm') and learn_fn in ['acktr', 'trpo_mpi', 'deepq']:
+            # TODO make acktr work with recurrent policies
+            # and test
+            # github issue: https://github.com/openai/baselines/issues/660
+            return
+
+    env = DummyVecEnv([lambda: gym.make('CartPole-v0')])
+    learn = get_learn_function(learn_fn)
+
+    kwargs = {}
+    kwargs.update(network_kwargs[network_fn])
+    kwargs.update(learn_kwargs[learn_fn])
+
+    learn =  partial(learn, env=env, network=network_fn, total_timesteps=0, **kwargs)
+    make_session(make_default=True, graph=tf.Graph());
+    model1 = learn(seed=1)
+    make_session(make_default=True, graph=tf.Graph());
+    model2 = learn(seed=2)
+
+    model1.step(env.observation_space.sample())
+    model2.step(env.observation_space.sample())
+
+
+
+def _serialize_variables():
+    sess = get_session()
+    variables = tf.trainable_variables()
+    values = sess.run(variables)
+    return {var.name: value for var, value in zip(variables, values)}
+
+
+def _get_action_stats(model, ob):
+    ntrials = 1000
+    if model.initial_state is None or model.initial_state == []:
+        actions = np.array([model.step(ob)[0] for _ in range(ntrials)])
+    else:
+        actions = np.array([model.step(ob, S=model.initial_state, M=[False])[0] for _ in range(ntrials)])
+
+    mean = np.mean(actions, axis=0)
+    std = np.std(actions, axis=0)
+
+    return mean, std
+

baselines/common/tests/util.py

@@ -0,0 +1,91 @@
+import tensorflow as tf
+import numpy as np
+from gym.spaces import np_random
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+
+N_TRIALS = 10000
+N_EPISODES = 100
+
+def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
+    np.random.seed(0)
+    np_random.seed(0)
+
+    env = DummyVecEnv([env_fn])
+
+
+    with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
+        tf.set_random_seed(0)
+
+        model = learn_fn(env)
+
+        sum_rew = 0
+        done = True
+
+        for i in range(n_trials):
+            if done:
+                obs = env.reset()
+                state = model.initial_state
+
+            if state is not None:
+                a, v, state, _ = model.step(obs, S=state, M=[False])
+            else:
+                a, v, _, _ = model.step(obs)
+
+            obs, rew, done, _ = env.step(a)
+            sum_rew += float(rew)
+
+        print("Reward in {} trials is {}".format(n_trials, sum_rew))
+        assert sum_rew > min_reward_fraction * n_trials, \
+            'sum of rewards {} is less than {} of the total number of trials {}'.format(sum_rew, min_reward_fraction, n_trials)
+
+
+
+def reward_per_episode_test(env_fn, learn_fn, min_avg_reward, n_trials=N_EPISODES):
+    env = DummyVecEnv([env_fn])
+
+    with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
+        model = learn_fn(env)
+
+        N_TRIALS = 100
+
+        observations, actions, rewards = rollout(env, model, N_TRIALS)
+        rewards = [sum(r) for r in rewards]
+
+        avg_rew = sum(rewards) / N_TRIALS
+        print("Average reward in {} episodes is {}".format(n_trials, avg_rew))
+        assert avg_rew > min_avg_reward, \
+            'average reward in {} episodes ({}) is less than {}'.format(n_trials, avg_rew, min_avg_reward)
+
+def rollout(env, model, n_trials):
+    rewards = []
+    actions = []
+    observations = []
+
+    for i in range(n_trials):
+        obs = env.reset()
+        state = model.initial_state
+        episode_rew = []
+        episode_actions = []
+        episode_obs = []
+
+        while True:
+            if state is not None:
+                a, v, state, _ = model.step(obs, S=state, M=[False])
+            else:
+                a,v, _, _ = model.step(obs)
+
+            obs, rew, done, _ = env.step(a)
+
+            episode_rew.append(rew)
+            episode_actions.append(a)
+            episode_obs.append(obs)
+
+            if done:
+                break
+
+        rewards.append(episode_rew)
+        actions.append(episode_actions)
+        observations.append(episode_obs)
+
+    return observations, actions, rewards
+

baselines/common/tf_util.py

@@ -1,3 +1,4 @@
+import joblib
 import numpy as np
 import tensorflow as tf  # pylint: ignore-module
 import copy
@@ -48,17 +49,28 @@ def huber_loss(x, delta=1.0):
 # Global session
 # ================================================================
 
-def make_session(num_cpu=None, make_default=False, graph=None):
+def get_session(config=None):
+    """Get default session or create one with a given config"""
+    sess = tf.get_default_session()
+    if sess is None:
+        sess = make_session(config=config, make_default=True)
+    return sess
+
+def make_session(config=None, num_cpu=None, make_default=False, graph=None):
     """Returns a session that will use <num_cpu> CPU's only"""
     if num_cpu is None:
         num_cpu = int(os.getenv('RCALL_NUM_CPU', multiprocessing.cpu_count()))
-    tf_config = tf.ConfigProto(
-        inter_op_parallelism_threads=num_cpu,
-        intra_op_parallelism_threads=num_cpu)
+    if config is None:
+        config = tf.ConfigProto(
+            allow_soft_placement=True,
+            inter_op_parallelism_threads=num_cpu,
+            intra_op_parallelism_threads=num_cpu)
+        config.gpu_options.allow_growth = True
+
     if make_default:
-        return tf.InteractiveSession(config=tf_config, graph=graph)
+        return tf.InteractiveSession(config=config, graph=graph)
     else:
-        return tf.Session(config=tf_config, graph=graph)
+        return tf.Session(config=config, graph=graph)
 
 def single_threaded_session():
     """Returns a session which will only use a single CPU"""
@@ -76,7 +88,7 @@ ALREADY_INITIALIZED = set()
 def initialize():
     """Initialize all the uninitialized variables in the global scope."""
     new_variables = set(tf.global_variables()) - ALREADY_INITIALIZED
-    tf.get_default_session().run(tf.variables_initializer(new_variables))
+    get_session().run(tf.variables_initializer(new_variables))
     ALREADY_INITIALIZED.update(new_variables)
 
 # ================================================================
@@ -85,7 +97,7 @@ def initialize():
 
 def normc_initializer(std=1.0, axis=0):
     def _initializer(shape, dtype=None, partition_info=None):  # pylint: disable=W0613
-        out = np.random.randn(*shape).astype(np.float32)
+        out = np.random.randn(*shape).astype(dtype.as_numpy_dtype)
         out *= std / np.sqrt(np.square(out).sum(axis=axis, keepdims=True))
         return tf.constant(out)
     return _initializer
@@ -179,7 +191,7 @@ class _Function(object):
         if hasattr(inpt, 'make_feed_dict'):
             feed_dict.update(inpt.make_feed_dict(value))
         else:
-            feed_dict[inpt] = value
+            feed_dict[inpt] = adjust_shape(inpt, value)
 
     def __call__(self, *args):
         assert len(args) <= len(self.inputs), "Too many arguments provided"
@@ -189,8 +201,8 @@ class _Function(object):
             self._feed_input(feed_dict, inpt, value)
         # Update feed dict with givens.
         for inpt in self.givens:
-            feed_dict[inpt] = feed_dict.get(inpt, self.givens[inpt])
-        results = tf.get_default_session().run(self.outputs_update, feed_dict=feed_dict)[:-1]
+            feed_dict[inpt] = adjust_shape(inpt, feed_dict.get(inpt, self.givens[inpt]))
+        results = get_session().run(self.outputs_update, feed_dict=feed_dict)[:-1]
         return results
 
 # ================================================================
@@ -243,27 +255,34 @@ class GetFlat(object):
     def __call__(self):
         return tf.get_default_session().run(self.op)
 
+def flattenallbut0(x):
+    return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])])
+
+# =============================================================
+# TF placeholders management
+# ============================================================
+
 _PLACEHOLDER_CACHE = {}  # name -> (placeholder, dtype, shape)
 
 def get_placeholder(name, dtype, shape):
     if name in _PLACEHOLDER_CACHE:
         out, dtype1, shape1 = _PLACEHOLDER_CACHE[name]
-        assert dtype1 == dtype and shape1 == shape
-        return out
-    else:
-        out = tf.placeholder(dtype=dtype, shape=shape, name=name)
-        _PLACEHOLDER_CACHE[name] = (out, dtype, shape)
-        return out
+        if out.graph == tf.get_default_graph():
+            assert dtype1 == dtype and shape1 == shape, \
+                'Placeholder with name {} has already been registered and has shape {}, different from requested {}'.format(name, shape1, shape)
+            return out
+
+    out = tf.placeholder(dtype=dtype, shape=shape, name=name)
+    _PLACEHOLDER_CACHE[name] = (out, dtype, shape)
+    return out
 
 def get_placeholder_cached(name):
     return _PLACEHOLDER_CACHE[name][0]
 
-def flattenallbut0(x):
-    return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])])
 
 
 # ================================================================
-# Diagnostics 
+# Diagnostics
 # ================================================================
 
 def display_var_info(vars):
@@ -274,7 +293,7 @@ def display_var_info(vars):
         if "/Adam" in name or "beta1_power" in name or "beta2_power" in name: continue
         v_params = np.prod(v.shape.as_list())
         count_params += v_params
-        if "/b:" in name or "/biases" in name: continue    # Wx+b, bias is not interesting to look at => count params, but not print
+        if "/b:" in name or "/bias" in name: continue    # Wx+b, bias is not interesting to look at => count params, but not print
         logger.info("   %s%s %i params %s" % (name, " "*(55-len(name)), v_params, str(v.shape)))
 
     logger.info("Total model parameters: %0.2f million" % (count_params*1e-6))
@@ -283,7 +302,7 @@ def display_var_info(vars):
 def get_available_gpus():
     # recipe from here:
     # https://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
- 
+
     from tensorflow.python.client import device_lib
     local_device_protos = device_lib.list_local_devices()
     return [x.name for x in local_device_protos if x.device_type == 'GPU']
@@ -292,13 +311,120 @@ def get_available_gpus():
 # Saving variables
 # ================================================================
 
-def load_state(fname):
+def load_state(fname, sess=None):
+    from baselines import logger
+    logger.warn('load_state method is deprecated, please use load_variables instead')
+    sess = sess or get_session()
     saver = tf.train.Saver()
     saver.restore(tf.get_default_session(), fname)
 
-def save_state(fname):
-    os.makedirs(os.path.dirname(fname), exist_ok=True)
+def save_state(fname, sess=None):
+    from baselines import logger
+    logger.warn('save_state method is deprecated, please use save_variables instead')
+    sess = sess or get_session()
+    dirname = os.path.dirname(fname)
+    if any(dirname):
+        os.makedirs(dirname, exist_ok=True)
     saver = tf.train.Saver()
     saver.save(tf.get_default_session(), fname)
 
+# The methods above and below are clearly doing the same thing, and in a rather similar way
+# TODO: ensure there is no subtle differences and remove one
 
+def save_variables(save_path, variables=None, sess=None):
+    sess = sess or get_session()
+    variables = variables or tf.trainable_variables()
+
+    ps = sess.run(variables)
+    save_dict = {v.name: value for v, value in zip(variables, ps)}
+    dirname = os.path.dirname(save_path)
+    if any(dirname):
+        os.makedirs(dirname, exist_ok=True)
+    joblib.dump(save_dict, save_path)
+
+def load_variables(load_path, variables=None, sess=None):
+    sess = sess or get_session()
+    variables = variables or tf.trainable_variables()
+
+    loaded_params = joblib.load(os.path.expanduser(load_path))
+    restores = []
+    if isinstance(loaded_params, list):
+        assert len(loaded_params) == len(variables), 'number of variables loaded mismatches len(variables)'
+        for d, v in zip(loaded_params, variables):
+            restores.append(v.assign(d))
+    else:
+        for v in variables:
+            restores.append(v.assign(loaded_params[v.name]))
+
+    sess.run(restores)
+
+# ================================================================
+# Shape adjustment for feeding into tf placeholders
+# ================================================================
+def adjust_shape(placeholder, data):
+    '''
+    adjust shape of the data to the shape of the placeholder if possible.
+    If shape is incompatible, AssertionError is thrown
+
+    Parameters:
+        placeholder     tensorflow input placeholder
+
+        data            input data to be (potentially) reshaped to be fed into placeholder
+
+    Returns:
+        reshaped data
+    '''
+
+    if not isinstance(data, np.ndarray) and not isinstance(data, list):
+        return data
+    if isinstance(data, list):
+        data = np.array(data)
+
+    placeholder_shape = [x or -1 for x in placeholder.shape.as_list()]
+
+    assert _check_shape(placeholder_shape, data.shape), \
+        'Shape of data {} is not compatible with shape of the placeholder {}'.format(data.shape, placeholder_shape)
+
+    return np.reshape(data, placeholder_shape)
+
+
+def _check_shape(placeholder_shape, data_shape):
+    ''' check if two shapes are compatible (i.e. differ only by dimensions of size 1, or by the batch dimension)'''
+
+    return True
+    squeezed_placeholder_shape = _squeeze_shape(placeholder_shape)
+    squeezed_data_shape = _squeeze_shape(data_shape)
+
+    for i, s_data in enumerate(squeezed_data_shape):
+        s_placeholder = squeezed_placeholder_shape[i]
+        if s_placeholder != -1 and s_data != s_placeholder:
+            return False
+
+    return True
+
+
+def _squeeze_shape(shape):
+    return [x for x in shape if x != 1]
+
+# ================================================================
+# Tensorboard interfacing
+# ================================================================
+
+def launch_tensorboard_in_background(log_dir):
+    '''
+    To log the Tensorflow graph when using rl-algs
+    algorithms, you can run the following code
+    in your main script:
+        import threading, time
+        def start_tensorboard(session):
+            time.sleep(10) # Wait until graph is setup
+            tb_path = osp.join(logger.get_dir(), 'tb')
+            summary_writer = tf.summary.FileWriter(tb_path, graph=session.graph)
+            summary_op = tf.summary.merge_all()
+            launch_tensorboard_in_background(tb_path)
+        session = tf.get_default_session()
+        t = threading.Thread(target=start_tensorboard, args=([session]))
+        t.start()
+    '''
+    import subprocess
+    subprocess.Popen(['tensorboard', '--logdir', log_dir])

baselines/common/vec_env/__init__.py

@@ -1,28 +1,37 @@
 from abc import ABC, abstractmethod
-from baselines import logger
+from baselines.common.tile_images import tile_images
 
 class AlreadySteppingError(Exception):
     """
     Raised when an asynchronous step is running while
     step_async() is called again.
     """
+
     def __init__(self):
         msg = 'already running an async step'
         Exception.__init__(self, msg)
 
+
 class NotSteppingError(Exception):
     """
     Raised when an asynchronous step is not running but
     step_wait() is called.
     """
+
     def __init__(self):
         msg = 'not running an async step'
         Exception.__init__(self, msg)
 
+
 class VecEnv(ABC):
     """
     An abstract asynchronous, vectorized environment.
+    Used to batch data from multiple copies of an environment, so that
+    each observation becomes an batch of observations, and expected action is a batch of actions to
+    be applied per-environment.
     """
+    closed = False
+    viewer = None
     def __init__(self, num_envs, observation_space, action_space):
         self.num_envs = num_envs
         self.observation_space = observation_space
@@ -32,7 +41,7 @@ class VecEnv(ABC):
     def reset(self):
         """
         Reset all the environments and return an array of
-        observations, or a tuple of observation arrays.
+        observations, or a dict of observation arrays.
 
         If step_async is still doing work, that work will
         be cancelled and step_wait() should not be called
@@ -58,7 +67,7 @@ class VecEnv(ABC):
         Wait for the step taken with step_async().
 
         Returns (obs, rews, dones, infos):
-         - obs: an array of observations, or a tuple of
+         - obs: an array of observations, or a dict of
                 arrays of observations.
          - rews: an array of rewards
          - dones: an array of "episode done" booleans
@@ -66,19 +75,46 @@ class VecEnv(ABC):
         """
         pass
 
-    @abstractmethod
-    def close(self):
+    def close_extras(self):
         """
-        Clean up the environments' resources.
+        Clean up the  extra resources, beyond what's in this base class.
+        Only runs when not self.closed.
         """
         pass
 
+    def close(self):
+        if self.closed:
+            return
+        if self.viewer is not None:
+            self.viewer.close()
+        self.close_extras()
+        self.closed = True
+
     def step(self, actions):
+        """
+        Step the environments synchronously.
+
+        This is available for backwards compatibility.
+        """
         self.step_async(actions)
         return self.step_wait()
 
     def render(self, mode='human'):
-        logger.warn('Render not defined for %s'%self)
+        imgs = self.get_images()
+        bigimg = tile_images(imgs)
+        if mode == 'human':
+            self.get_viewer().imshow(bigimg)
+            return self.get_viewer().isopen
+        elif mode == 'rgb_array':
+            return bigimg
+        else:
+            raise NotImplementedError
+
+    def get_images(self):
+        """
+        Return RGB images from each environment
+        """
+        raise NotImplementedError
 
     @property
     def unwrapped(self):
@@ -87,13 +123,25 @@ class VecEnv(ABC):
         else:
             return self
 
+    def get_viewer(self):
+        if self.viewer is None:
+            from gym.envs.classic_control import rendering
+            self.viewer = rendering.SimpleImageViewer()
+        return self.viewer
+
+
 class VecEnvWrapper(VecEnv):
+    """
+    An environment wrapper that applies to an entire batch
+    of environments at once.
+    """
+
     def __init__(self, venv, observation_space=None, action_space=None):
         self.venv = venv
-        VecEnv.__init__(self, 
-            num_envs=venv.num_envs,
-            observation_space=observation_space or venv.observation_space, 
-            action_space=action_space or venv.action_space)
+        VecEnv.__init__(self,
+                        num_envs=venv.num_envs,
+                        observation_space=observation_space or venv.observation_space,
+                        action_space=action_space or venv.action_space)
 
     def step_async(self, actions):
         self.venv.step_async(actions)
@@ -109,18 +157,24 @@ class VecEnvWrapper(VecEnv):
     def close(self):
         return self.venv.close()
 
-    def render(self):
-        self.venv.render()
+    def render(self, mode='human'):
+        return self.venv.render(mode=mode)
+
+    def get_images(self):
+        return self.venv.get_images()
 
 class CloudpickleWrapper(object):
     """
     Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
     """
+
     def __init__(self, x):
         self.x = x
+
     def __getstate__(self):
         import cloudpickle
         return cloudpickle.dumps(self.x)
+
     def __setstate__(self, ob):
         import pickle
         self.x = pickle.loads(ob)

baselines/common/vec_env/dummy_vec_env.py

@@ -1,28 +1,27 @@
 import numpy as np
 from gym import spaces
-from collections import OrderedDict
 from . import VecEnv
+from .util import copy_obs_dict, dict_to_obs, obs_space_info
 
 class DummyVecEnv(VecEnv):
+    """
+    VecEnv that does runs multiple environments sequentially, that is,
+    the step and reset commands are send to one environment at a time.
+    Useful when debugging and when num_env == 1 (in the latter case,
+    avoids communication overhead)
+    """
     def __init__(self, env_fns):
+        """
+        Arguments:
+
+        env_fns: iterable of callables      functions that build environments
+        """
         self.envs = [fn() for fn in env_fns]
         env = self.envs[0]
         VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
-        shapes, dtypes = {}, {}
-        self.keys = []
         obs_space = env.observation_space
 
-        if isinstance(obs_space, spaces.Dict):
-            assert isinstance(obs_space.spaces, OrderedDict)
-            subspaces = obs_space.spaces
-        else:
-            subspaces = {None: obs_space}
-
-        for key, box in subspaces.items():
-            shapes[key] = box.shape
-            dtypes[key] = box.dtype
-            self.keys.append(key)
-        
+        self.keys, shapes, dtypes = obs_space_info(obs_space)
         self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
         self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
         self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
@@ -30,11 +29,26 @@ class DummyVecEnv(VecEnv):
         self.actions = None
 
     def step_async(self, actions):
-        self.actions = actions
+        listify = True
+        try:
+            if len(actions) == self.num_envs:
+                listify = False
+        except TypeError:
+            pass
+
+        if not listify:
+            self.actions = actions
+        else:
+            assert self.num_envs == 1, "actions {} is either not a list or has a wrong size - cannot match to {} environments".format(actions, self.num_envs)
+            self.actions = [actions]
 
     def step_wait(self):
         for e in range(self.num_envs):
-            obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(self.actions[e])
+            action = self.actions[e]
+            if isinstance(self.envs[e].action_space, spaces.Discrete):
+                action = int(action)
+
+            obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
             if self.buf_dones[e]:
                 obs = self.envs[e].reset()
             self._save_obs(e, obs)
@@ -47,12 +61,6 @@ class DummyVecEnv(VecEnv):
             self._save_obs(e, obs)
         return self._obs_from_buf()
 
-    def close(self):
-        return
-
-    def render(self, mode='human'):
-        return [e.render(mode=mode) for e in self.envs]
-
     def _save_obs(self, e, obs):
         for k in self.keys:
             if k is None:
@@ -61,7 +69,13 @@ class DummyVecEnv(VecEnv):
                 self.buf_obs[k][e] = obs[k]
 
     def _obs_from_buf(self):
-        if self.keys==[None]:
-            return self.buf_obs[None]
+        return dict_to_obs(copy_obs_dict(self.buf_obs))
+
+    def get_images(self):
+        return [env.render(mode='rgb_array') for env in self.envs]
+
+    def render(self, mode='human'):
+        if self.num_envs == 1:
+            self.envs[0].render(mode=mode)
         else:
-            return self.buf_obs
+            super().render(mode=mode)

baselines/common/vec_env/shmem_vec_env.py

@@ -0,0 +1,137 @@
+"""
+An interface for asynchronous vectorized environments.
+"""
+
+from multiprocessing import Pipe, Array, Process
+import numpy as np
+from . import VecEnv, CloudpickleWrapper
+import ctypes
+from baselines import logger
+
+from .util import dict_to_obs, obs_space_info, obs_to_dict
+
+_NP_TO_CT = {np.float32: ctypes.c_float,
+             np.int32: ctypes.c_int32,
+             np.int8: ctypes.c_int8,
+             np.uint8: ctypes.c_char,
+             np.bool: ctypes.c_bool}
+
+
+class ShmemVecEnv(VecEnv):
+    """
+    Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
+    """
+
+    def __init__(self, env_fns, spaces=None):
+        """
+        If you don't specify observation_space, we'll have to create a dummy
+        environment to get it.
+        """
+        if spaces:
+            observation_space, action_space = spaces
+        else:
+            logger.log('Creating dummy env object to get spaces')
+            with logger.scoped_configure(format_strs=[]):
+                dummy = env_fns[0]()
+                observation_space, action_space = dummy.observation_space, dummy.action_space
+                dummy.close()
+                del dummy
+        VecEnv.__init__(self, len(env_fns), observation_space, action_space)
+        self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
+        self.obs_bufs = [
+            {k: Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
+            for _ in env_fns]
+        self.parent_pipes = []
+        self.procs = []
+        for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
+            wrapped_fn = CloudpickleWrapper(env_fn)
+            parent_pipe, child_pipe = Pipe()
+            proc = Process(target=_subproc_worker,
+                           args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
+            proc.daemon = True
+            self.procs.append(proc)
+            self.parent_pipes.append(parent_pipe)
+            proc.start()
+            child_pipe.close()
+        self.waiting_step = False
+        self.viewer = None
+
+    def reset(self):
+        if self.waiting_step:
+            logger.warn('Called reset() while waiting for the step to complete')
+            self.step_wait()
+        for pipe in self.parent_pipes:
+            pipe.send(('reset', None))
+        return self._decode_obses([pipe.recv() for pipe in self.parent_pipes])
+
+    def step_async(self, actions):
+        assert len(actions) == len(self.parent_pipes)
+        for pipe, act in zip(self.parent_pipes, actions):
+            pipe.send(('step', act))
+
+    def step_wait(self):
+        outs = [pipe.recv() for pipe in self.parent_pipes]
+        obs, rews, dones, infos = zip(*outs)
+        return self._decode_obses(obs), np.array(rews), np.array(dones), infos
+
+    def close_extras(self):
+        if self.waiting_step:
+            self.step_wait()
+        for pipe in self.parent_pipes:
+            pipe.send(('close', None))
+        for pipe in self.parent_pipes:
+            pipe.recv()
+            pipe.close()
+        for proc in self.procs:
+            proc.join()
+
+    def get_images(self, mode='human'):
+        for pipe in self.parent_pipes:
+            pipe.send(('render', None))
+        return [pipe.recv() for pipe in self.parent_pipes]
+
+    def _decode_obses(self, obs):
+        result = {}
+        for k in self.obs_keys:
+
+            bufs = [b[k] for b in self.obs_bufs]
+            o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs]
+            result[k] = np.array(o)
+        return dict_to_obs(result)
+
+
+def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
+    """
+    Control a single environment instance using IPC and
+    shared memory.
+    """
+    def _write_obs(maybe_dict_obs):
+        flatdict = obs_to_dict(maybe_dict_obs)
+        for k in keys:
+            dst = obs_bufs[k].get_obj()
+            dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k])  # pylint: disable=W0212
+            np.copyto(dst_np, flatdict[k])
+
+    env = env_fn_wrapper.x()
+    parent_pipe.close()
+    try:
+        while True:
+            cmd, data = pipe.recv()
+            if cmd == 'reset':
+                pipe.send(_write_obs(env.reset()))
+            elif cmd == 'step':
+                obs, reward, done, info = env.step(data)
+                if done:
+                    obs = env.reset()
+                pipe.send((_write_obs(obs), reward, done, info))
+            elif cmd == 'render':
+                pipe.send(env.render(mode='rgb_array'))
+            elif cmd == 'close':
+                pipe.send(None)
+                break
+            else:
+                raise RuntimeError('Got unrecognized cmd %s' % cmd)
+    except KeyboardInterrupt:
+        print('ShmemVecEnv worker: got KeyboardInterrupt')
+    finally:
+        env.close()

baselines/common/vec_env/subproc_vec_env.py

@@ -1,97 +1,99 @@
 import numpy as np
 from multiprocessing import Process, Pipe
-from baselines.common.vec_env import VecEnv, CloudpickleWrapper
-from baselines.common.tile_images import tile_images
-
+from . import VecEnv, CloudpickleWrapper
 
 def worker(remote, parent_remote, env_fn_wrapper):
     parent_remote.close()
     env = env_fn_wrapper.x()
-    while True:
-        cmd, data = remote.recv()
-        if cmd == 'step':
-            ob, reward, done, info = env.step(data)
-            if done:
+    try:
+        while True:
+            cmd, data = remote.recv()
+            if cmd == 'step':
+                ob, reward, done, info = env.step(data)
+                if done:
+                    ob = env.reset()
+                remote.send((ob, reward, done, info))
+            elif cmd == 'reset':
                 ob = env.reset()
-            remote.send((ob, reward, done, info))
-        elif cmd == 'reset':
-            ob = env.reset()
-            remote.send(ob)
-        elif cmd == 'render':
-            remote.send(env.render(mode='rgb_array'))
-        elif cmd == 'close':
-            remote.close()
-            break
-        elif cmd == 'get_spaces':
-            remote.send((env.observation_space, env.action_space))
-        else:
-            raise NotImplementedError
+                remote.send(ob)
+            elif cmd == 'render':
+                remote.send(env.render(mode='rgb_array'))
+            elif cmd == 'close':
+                remote.close()
+                break
+            elif cmd == 'get_spaces':
+                remote.send((env.observation_space, env.action_space))
+            else:
+                raise NotImplementedError
+    except KeyboardInterrupt:
+        print('SubprocVecEnv worker: got KeyboardInterrupt')
+    finally:
+        env.close()
 
 
 class SubprocVecEnv(VecEnv):
+    """
+    VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
+    Recommended to use when num_envs > 1 and step() can be a bottleneck.
+    """
     def __init__(self, env_fns, spaces=None):
         """
-        envs: list of gym environments to run in subprocesses
+        Arguments:
+
+        env_fns: iterable of callables -  functions that create environments to run in subprocesses. Need to be cloud-pickleable
         """
         self.waiting = False
         self.closed = False
         nenvs = len(env_fns)
         self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
         self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
-            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
+                   for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
         for p in self.ps:
-            p.daemon = True # if the main process crashes, we should not cause things to hang
+            p.daemon = True  # if the main process crashes, we should not cause things to hang
             p.start()
         for remote in self.work_remotes:
             remote.close()
 
         self.remotes[0].send(('get_spaces', None))
         observation_space, action_space = self.remotes[0].recv()
+        self.viewer = None
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
 
     def step_async(self, actions):
+        self._assert_not_closed()
         for remote, action in zip(self.remotes, actions):
             remote.send(('step', action))
         self.waiting = True
 
     def step_wait(self):
+        self._assert_not_closed()
         results = [remote.recv() for remote in self.remotes]
         self.waiting = False
         obs, rews, dones, infos = zip(*results)
         return np.stack(obs), np.stack(rews), np.stack(dones), infos
 
     def reset(self):
+        self._assert_not_closed()
         for remote in self.remotes:
             remote.send(('reset', None))
         return np.stack([remote.recv() for remote in self.remotes])
 
-    def reset_task(self):
-        for remote in self.remotes:
-            remote.send(('reset_task', None))
-        return np.stack([remote.recv() for remote in self.remotes])
-
-    def close(self):
-        if self.closed:
-            return
+    def close_extras(self):
+        self.closed = True
         if self.waiting:
-            for remote in self.remotes:            
+            for remote in self.remotes:
                 remote.recv()
         for remote in self.remotes:
             remote.send(('close', None))
         for p in self.ps:
             p.join()
-        self.closed = True
 
-    def render(self, mode='human'):
+    def get_images(self):
+        self._assert_not_closed()
         for pipe in self.remotes:
             pipe.send(('render', None))
         imgs = [pipe.recv() for pipe in self.remotes]
-        bigimg = tile_images(imgs)
-        if mode == 'human':
-            import cv2
-            cv2.imshow('vecenv', bigimg[:,:,::-1])
-            cv2.waitKey(1)
-        elif mode == 'rgb_array':
-            return bigimg
-        else:
-            raise NotImplementedError
+        return imgs
+
+    def _assert_not_closed(self):
+        assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"

baselines/common/vec_env/test_vec_env.py

@@ -0,0 +1,101 @@
+"""
+Tests for asynchronous vectorized environments.
+"""
+
+import gym
+import numpy as np
+import pytest
+from .dummy_vec_env import DummyVecEnv
+from .shmem_vec_env import ShmemVecEnv
+from .subproc_vec_env import SubprocVecEnv
+
+
+def assert_envs_equal(env1, env2, num_steps):
+    """
+    Compare two environments over num_steps steps and make sure
+    that the observations produced by each are the same when given
+    the same actions.
+    """
+    assert env1.num_envs == env2.num_envs
+    assert env1.action_space.shape == env2.action_space.shape
+    assert env1.action_space.dtype == env2.action_space.dtype
+    joint_shape = (env1.num_envs,) + env1.action_space.shape
+
+    try:
+        obs1, obs2 = env1.reset(), env2.reset()
+        assert np.array(obs1).shape == np.array(obs2).shape
+        assert np.array(obs1).shape == joint_shape
+        assert np.allclose(obs1, obs2)
+        np.random.seed(1337)
+        for _ in range(num_steps):
+            actions = np.array(np.random.randint(0, 0x100, size=joint_shape),
+                               dtype=env1.action_space.dtype)
+            for env in [env1, env2]:
+                env.step_async(actions)
+            outs1 = env1.step_wait()
+            outs2 = env2.step_wait()
+            for out1, out2 in zip(outs1[:3], outs2[:3]):
+                assert np.array(out1).shape == np.array(out2).shape
+                assert np.allclose(out1, out2)
+            assert list(outs1[3]) == list(outs2[3])
+    finally:
+        env1.close()
+        env2.close()
+
+
+@pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv))
+@pytest.mark.parametrize('dtype', ('uint8', 'float32'))
+def test_vec_env(klass, dtype):  # pylint: disable=R0914
+    """
+    Test that a vectorized environment is equivalent to
+    DummyVecEnv, since DummyVecEnv is less likely to be
+    error prone.
+    """
+    num_envs = 3
+    num_steps = 100
+    shape = (3, 8)
+
+    def make_fn(seed):
+        """
+        Get an environment constructor with a seed.
+        """
+        return lambda: SimpleEnv(seed, shape, dtype)
+    fns = [make_fn(i) for i in range(num_envs)]
+    env1 = DummyVecEnv(fns)
+    env2 = klass(fns)
+    assert_envs_equal(env1, env2, num_steps=num_steps)
+
+
+class SimpleEnv(gym.Env):
+    """
+    An environment with a pre-determined observation space
+    and RNG seed.
+    """
+
+    def __init__(self, seed, shape, dtype):
+        np.random.seed(seed)
+        self._dtype = dtype
+        self._start_obs = np.array(np.random.randint(0, 0x100, size=shape),
+                                   dtype=dtype)
+        self._max_steps = seed + 1
+        self._cur_obs = None
+        self._cur_step = 0
+        # this is 0xFF instead of 0x100 because the Box space includes
+        # the high end, while randint does not
+        self.action_space = gym.spaces.Box(low=0, high=0xFF, shape=shape, dtype=dtype)
+        self.observation_space = self.action_space
+
+    def step(self, action):
+        self._cur_obs += np.array(action, dtype=self._dtype)
+        self._cur_step += 1
+        done = self._cur_step >= self._max_steps
+        reward = self._cur_step / self._max_steps
+        return self._cur_obs, reward, done, {'foo': 'bar' + str(reward)}
+
+    def reset(self):
+        self._cur_obs = self._start_obs
+        self._cur_step = 0
+        return self._cur_obs
+
+    def render(self, mode=None):
+        raise NotImplementedError

baselines/common/vec_env/util.py

@@ -0,0 +1,59 @@
+"""
+Helpers for dealing with vectorized environments.
+"""
+
+from collections import OrderedDict
+
+import gym
+import numpy as np
+
+
+def copy_obs_dict(obs):
+    """
+    Deep-copy an observation dict.
+    """
+    return {k: np.copy(v) for k, v in obs.items()}
+
+
+def dict_to_obs(obs_dict):
+    """
+    Convert an observation dict into a raw array if the
+    original observation space was not a Dict space.
+    """
+    if set(obs_dict.keys()) == {None}:
+        return obs_dict[None]
+    return obs_dict
+
+
+def obs_space_info(obs_space):
+    """
+    Get dict-structured information about a gym.Space.
+
+    Returns:
+      A tuple (keys, shapes, dtypes):
+        keys: a list of dict keys.
+        shapes: a dict mapping keys to shapes.
+        dtypes: a dict mapping keys to dtypes.
+    """
+    if isinstance(obs_space, gym.spaces.Dict):
+        assert isinstance(obs_space.spaces, OrderedDict)
+        subspaces = obs_space.spaces
+    else:
+        subspaces = {None: obs_space}
+    keys = []
+    shapes = {}
+    dtypes = {}
+    for key, box in subspaces.items():
+        keys.append(key)
+        shapes[key] = box.shape
+        dtypes[key] = box.dtype
+    return keys, shapes, dtypes
+
+
+def obs_to_dict(obs):
+    """
+    Convert an observation into a dict.
+    """
+    if isinstance(obs, dict):
+        return obs
+    return {None: obs}

baselines/common/vec_env/vec_frame_stack.py

@@ -1,18 +1,16 @@
-from baselines.common.vec_env import VecEnvWrapper
+from . import VecEnvWrapper
 import numpy as np
 from gym import spaces
 
+
 class VecFrameStack(VecEnvWrapper):
-    """
-    Vectorized environment base class
-    """
     def __init__(self, venv, nstack):
         self.venv = venv
         self.nstack = nstack
-        wos = venv.observation_space # wrapped ob space
+        wos = venv.observation_space  # wrapped ob space
         low = np.repeat(wos.low, self.nstack, axis=-1)
         high = np.repeat(wos.high, self.nstack, axis=-1)
-        self.stackedobs = np.zeros((venv.num_envs,)+low.shape, low.dtype)
+        self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype)
         observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype)
         VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
 
@@ -26,13 +24,7 @@ class VecFrameStack(VecEnvWrapper):
         return self.stackedobs, rews, news, infos
 
     def reset(self):
-        """
-        Reset all environments
-        """
         obs = self.venv.reset()
         self.stackedobs[...] = 0
         self.stackedobs[..., -obs.shape[-1]:] = obs
         return self.stackedobs
-
-    def close(self):
-        self.venv.close()

baselines/common/vec_env/vec_monitor.py

@@ -0,0 +1,37 @@
+from . import VecEnvWrapper
+from baselines.bench.monitor import ResultsWriter
+import numpy as np
+import time
+
+
+class VecMonitor(VecEnvWrapper):
+    def __init__(self, venv, filename=None):
+        VecEnvWrapper.__init__(self, venv)
+        self.eprets = None
+        self.eplens = None
+        self.tstart = time.time()
+        self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
+
+    def reset(self):
+        obs = self.venv.reset()
+        self.eprets = np.zeros(self.num_envs, 'f')
+        self.eplens = np.zeros(self.num_envs, 'i')
+        return obs
+
+    def step_wait(self):
+        obs, rews, dones, infos = self.venv.step_wait()
+        self.eprets += rews
+        self.eplens += 1
+        newinfos = []
+        for (i, (done, ret, eplen, info)) in enumerate(zip(dones, self.eprets, self.eplens, infos)):
+            info = info.copy()
+            if done:
+                epinfo = {'r': ret, 'l': eplen, 't': round(time.time() - self.tstart, 6)}
+                info['episode'] = epinfo
+                self.eprets[i] = 0
+                self.eplens[i] = 0
+                self.results_writer.write_row(epinfo)
+
+            newinfos.append(info)
+
+        return obs, rews, dones, newinfos

baselines/common/vec_env/vec_normalize.py

@@ -1,11 +1,14 @@
-from baselines.common.vec_env import VecEnvWrapper
+from . import VecEnvWrapper
 from baselines.common.running_mean_std import RunningMeanStd
 import numpy as np
 
+
 class VecNormalize(VecEnvWrapper):
     """
-    Vectorized environment base class
+    A vectorized wrapper that normalizes the observations
+    and returns from an environment.
     """
+
     def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
         VecEnvWrapper.__init__(self, venv)
         self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
@@ -17,18 +20,13 @@ class VecNormalize(VecEnvWrapper):
         self.epsilon = epsilon
 
     def step_wait(self):
-        """
-        Apply sequence of actions to sequence of environments
-        actions -> (observations, rewards, news)
-
-        where 'news' is a boolean vector indicating whether each element is new.
-        """
         obs, rews, news, infos = self.venv.step_wait()
         self.ret = self.ret * self.gamma + rews
         obs = self._obfilt(obs)
         if self.ret_rms:
             self.ret_rms.update(self.ret)
             rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
+        self.ret[news] = 0.
         return obs, rews, news, infos
 
     def _obfilt(self, obs):
@@ -40,8 +38,6 @@ class VecNormalize(VecEnvWrapper):
             return obs
 
     def reset(self):
-        """
-        Reset all environments
-        """
+        self.ret = np.zeros(self.num_envs)
         obs = self.venv.reset()
         return self._obfilt(obs)

baselines/ddpg/README.md

@@ -2,4 +2,4 @@
 
 - Original paper: https://arxiv.org/abs/1509.02971
 - Baselines post: https://blog.openai.com/better-exploration-with-parameter-noise/
-- `python -m baselines.ddpg.main` runs the algorithm for 1M frames = 10M timesteps on a Mujoco environment. See help (`-h`) for more options.
+- `python -m baselines.run --alg=ddpg --env=HalfCheetah-v2 --num_timesteps=1e6` runs the algorithm for 1M frames = 10M timesteps on a Mujoco environment. See help (`-h`) for more options.

baselines/ddpg/ddpg.py

@@ -1,378 +1,259 @@
-from copy import copy
-from functools import reduce
+import os
+import time
+from collections import deque
+import pickle
 
-import numpy as np
-import tensorflow as tf
-import tensorflow.contrib as tc
+from baselines.ddpg.ddpg_learner import DDPG
+from baselines.ddpg.models import Actor, Critic
+from baselines.ddpg.memory import Memory
+from baselines.ddpg.noise import AdaptiveParamNoiseSpec, NormalActionNoise, OrnsteinUhlenbeckActionNoise
 
-from baselines import logger
-from baselines.common.mpi_adam import MpiAdam
 import baselines.common.tf_util as U
-from baselines.common.mpi_running_mean_std import RunningMeanStd
+
+from baselines import logger, registry
+import numpy as np
 from mpi4py import MPI
 
-def normalize(x, stats):
-    if stats is None:
-        return x
-    return (x - stats.mean) / stats.std
+@registry.register('ddpg')
+def learn(network, env,
+          seed=None,
+          total_timesteps=None,
+          nb_epochs=None, # with default settings, perform 1M steps total
+          nb_epoch_cycles=20,
+          nb_rollout_steps=100,
+          reward_scale=1.0,
+          render=False,
+          render_eval=False,
+          noise_type='adaptive-param_0.2',
+          normalize_returns=False,
+          normalize_observations=True,
+          critic_l2_reg=1e-2,
+          actor_lr=1e-4,
+          critic_lr=1e-3,
+          popart=False,
+          gamma=0.99,
+          clip_norm=None,
+          nb_train_steps=50, # per epoch cycle and MPI worker,
+          nb_eval_steps=100,
+          batch_size=64, # per MPI worker
+          tau=0.01,
+          eval_env=None,
+          param_noise_adaption_interval=50,
+          **network_kwargs):
 
 
-def denormalize(x, stats):
-    if stats is None:
-        return x
-    return x * stats.std + stats.mean
+    if total_timesteps is not None:
+        assert nb_epochs is None
+        nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps)
+    else:
+        nb_epochs = 500
 
-def reduce_std(x, axis=None, keepdims=False):
-    return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims))
+    rank = MPI.COMM_WORLD.Get_rank()
+    nb_actions = env.action_space.shape[-1]
+    assert (np.abs(env.action_space.low) == env.action_space.high).all()  # we assume symmetric actions.
 
-def reduce_var(x, axis=None, keepdims=False):
-    m = tf.reduce_mean(x, axis=axis, keep_dims=True)
-    devs_squared = tf.square(x - m)
-    return tf.reduce_mean(devs_squared, axis=axis, keep_dims=keepdims)
+    memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
+    critic = Critic(network=network, **network_kwargs)
+    actor = Actor(nb_actions, network=network, **network_kwargs)
 
-def get_target_updates(vars, target_vars, tau):
-    logger.info('setting up target updates ...')
-    soft_updates = []
-    init_updates = []
-    assert len(vars) == len(target_vars)
-    for var, target_var in zip(vars, target_vars):
-        logger.info('  {} <- {}'.format(target_var.name, var.name))
-        init_updates.append(tf.assign(target_var, var))
-        soft_updates.append(tf.assign(target_var, (1. - tau) * target_var + tau * var))
-    assert len(init_updates) == len(vars)
-    assert len(soft_updates) == len(vars)
-    return tf.group(*init_updates), tf.group(*soft_updates)
+    action_noise = None
+    param_noise = None
+    nb_actions = env.action_space.shape[-1]
+    if noise_type is not None:
+        for current_noise_type in noise_type.split(','):
+            current_noise_type = current_noise_type.strip()
+            if current_noise_type == 'none':
+                pass
+            elif 'adaptive-param' in current_noise_type:
+                _, stddev = current_noise_type.split('_')
+                param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
+            elif 'normal' in current_noise_type:
+                _, stddev = current_noise_type.split('_')
+                action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+            elif 'ou' in current_noise_type:
+                _, stddev = current_noise_type.split('_')
+                action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
+            else:
+                raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
+
+    max_action = env.action_space.high
+    logger.info('scaling actions by {} before executing in env'.format(max_action))
+
+    agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
+        gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,
+        batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,
+        actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm,
+        reward_scale=reward_scale)
+    logger.info('Using agent with the following configuration:')
+    logger.info(str(agent.__dict__.items()))
+
+    eval_episode_rewards_history = deque(maxlen=100)
+    episode_rewards_history = deque(maxlen=100)
+    sess = U.get_session()
+    # Prepare everything.
+    agent.initialize(sess)
+    sess.graph.finalize()
+
+    agent.reset()
+
+    obs = env.reset()
+    if eval_env is not None:
+        eval_obs = eval_env.reset()
+    nenvs = obs.shape[0]
+
+    episode_reward = np.zeros(nenvs, dtype = np.float32) #vector
+    episode_step = np.zeros(nenvs, dtype = int) # vector
+    episodes = 0 #scalar
+    t = 0 # scalar
+
+    epoch = 0
 
 
-def get_perturbed_actor_updates(actor, perturbed_actor, param_noise_stddev):
-    assert len(actor.vars) == len(perturbed_actor.vars)
-    assert len(actor.perturbable_vars) == len(perturbed_actor.perturbable_vars)
 
-    updates = []
-    for var, perturbed_var in zip(actor.vars, perturbed_actor.vars):
-        if var in actor.perturbable_vars:
-            logger.info('  {} <- {} + noise'.format(perturbed_var.name, var.name))
-            updates.append(tf.assign(perturbed_var, var + tf.random_normal(tf.shape(var), mean=0., stddev=param_noise_stddev)))
-        else:
-            logger.info('  {} <- {}'.format(perturbed_var.name, var.name))
-            updates.append(tf.assign(perturbed_var, var))
-    assert len(updates) == len(actor.vars)
-    return tf.group(*updates)
+    start_time = time.time()
+
+    epoch_episode_rewards = []
+    epoch_episode_steps = []
+    epoch_actions = []
+    epoch_qs = []
+    epoch_episodes = 0
+    for epoch in range(nb_epochs):
+        for cycle in range(nb_epoch_cycles):
+            # Perform rollouts.
+            if nenvs > 1:
+                # if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
+                # of the environments, so resetting here instead
+                agent.reset()
+            for t_rollout in range(nb_rollout_steps):
+                # Predict next action.
+                action, q, _, _ = agent.step(obs, apply_noise=True, compute_Q=True)
+
+                # Execute next action.
+                if rank == 0 and render:
+                    env.render()
+
+                # max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch
+                new_obs, r, done, info = env.step(max_action * action)  # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
+                # note these outputs are batched from vecenv
+
+                t += 1
+                if rank == 0 and render:
+                    env.render()
+                episode_reward += r
+                episode_step += 1
+
+                # Book-keeping.
+                epoch_actions.append(action)
+                epoch_qs.append(q)
+                agent.store_transition(obs, action, r, new_obs, done) #the batched data will be unrolled in memory.py's append.
+
+                obs = new_obs
+
+                for d in range(len(done)):
+                    if done[d]:
+                        # Episode done.
+                        epoch_episode_rewards.append(episode_reward[d])
+                        episode_rewards_history.append(episode_reward[d])
+                        epoch_episode_steps.append(episode_step[d])
+                        episode_reward[d] = 0.
+                        episode_step[d] = 0
+                        epoch_episodes += 1
+                        episodes += 1
+                        if nenvs == 1:
+                            agent.reset()
 
 
-class DDPG(object):
-    def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
-        gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
-        batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
-        adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
-        critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
-        # Inputs.
-        self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
-        self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
-        self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
-        self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
-        self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
-        self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
-        self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
 
-        # Parameters.
-        self.gamma = gamma
-        self.tau = tau
-        self.memory = memory
-        self.normalize_observations = normalize_observations
-        self.normalize_returns = normalize_returns
-        self.action_noise = action_noise
-        self.param_noise = param_noise
-        self.action_range = action_range
-        self.return_range = return_range
-        self.observation_range = observation_range
-        self.critic = critic
-        self.actor = actor
-        self.actor_lr = actor_lr
-        self.critic_lr = critic_lr
-        self.clip_norm = clip_norm
-        self.enable_popart = enable_popart
-        self.reward_scale = reward_scale
-        self.batch_size = batch_size
-        self.stats_sample = None
-        self.critic_l2_reg = critic_l2_reg
+            # Train.
+            epoch_actor_losses = []
+            epoch_critic_losses = []
+            epoch_adaptive_distances = []
+            for t_train in range(nb_train_steps):
+                # Adapt param noise, if necessary.
+                if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
+                    distance = agent.adapt_param_noise()
+                    epoch_adaptive_distances.append(distance)
 
-        # Observation normalization.
-        if self.normalize_observations:
-            with tf.variable_scope('obs_rms'):
-                self.obs_rms = RunningMeanStd(shape=observation_shape)
-        else:
-            self.obs_rms = None
-        normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
-            self.observation_range[0], self.observation_range[1])
-        normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
-            self.observation_range[0], self.observation_range[1])
+                cl, al = agent.train()
+                epoch_critic_losses.append(cl)
+                epoch_actor_losses.append(al)
+                agent.update_target_net()
 
-        # Return normalization.
-        if self.normalize_returns:
-            with tf.variable_scope('ret_rms'):
-                self.ret_rms = RunningMeanStd()
-        else:
-            self.ret_rms = None
+            # Evaluate.
+            eval_episode_rewards = []
+            eval_qs = []
+            if eval_env is not None:
+                nenvs_eval = eval_obs.shape[0]
+                eval_episode_reward = np.zeros(nenvs_eval, dtype = np.float32)
+                for t_rollout in range(nb_eval_steps):
+                    eval_action, eval_q, _, _ = agent.step(eval_obs, apply_noise=False, compute_Q=True)
+                    eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action)  # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
+                    if render_eval:
+                        eval_env.render()
+                    eval_episode_reward += eval_r
 
-        # Create target networks.
-        target_actor = copy(actor)
-        target_actor.name = 'target_actor'
-        self.target_actor = target_actor
-        target_critic = copy(critic)
-        target_critic.name = 'target_critic'
-        self.target_critic = target_critic
+                    eval_qs.append(eval_q)
+                    for d in range(len(eval_done)):
+                        if eval_done[d]:
+                            eval_episode_rewards.append(eval_episode_reward[d])
+                            eval_episode_rewards_history.append(eval_episode_reward[d])
+                            eval_episode_reward[d] = 0.0
 
-        # Create networks and core TF parts that are shared across setup parts.
-        self.actor_tf = actor(normalized_obs0)
-        self.normalized_critic_tf = critic(normalized_obs0, self.actions)
-        self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
-        self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
-        self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
-        Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
-        self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
+        mpi_size = MPI.COMM_WORLD.Get_size()
+        # Log stats.
+        # XXX shouldn't call np.mean on variable length lists
+        duration = time.time() - start_time
+        stats = agent.get_stats()
+        combined_stats = stats.copy()
+        combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
+        combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
+        combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
+        combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
+        combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
+        combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
+        combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
+        combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
+        combined_stats['total/duration'] = duration
+        combined_stats['total/steps_per_second'] = float(t) / float(duration)
+        combined_stats['total/episodes'] = episodes
+        combined_stats['rollout/episodes'] = epoch_episodes
+        combined_stats['rollout/actions_std'] = np.std(epoch_actions)
+        # Evaluation statistics.
+        if eval_env is not None:
+            combined_stats['eval/return'] = eval_episode_rewards
+            combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
+            combined_stats['eval/Q'] = eval_qs
+            combined_stats['eval/episodes'] = len(eval_episode_rewards)
+        def as_scalar(x):
+            if isinstance(x, np.ndarray):
+                assert x.size == 1
+                return x[0]
+            elif np.isscalar(x):
+                return x
+            else:
+                raise ValueError('expected scalar, got %s'%x)
 
-        # Set up parts.
-        if self.param_noise is not None:
-            self.setup_param_noise(normalized_obs0)
-        self.setup_actor_optimizer()
-        self.setup_critic_optimizer()
-        if self.normalize_returns and self.enable_popart:
-            self.setup_popart()
-        self.setup_stats()
-        self.setup_target_network_updates()
+        combined_stats_sums = MPI.COMM_WORLD.allreduce(np.array([ np.array(x).flatten()[0] for x in combined_stats.values()]))
+        combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
 
-    def setup_target_network_updates(self):
-        actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
-        critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
-        self.target_init_updates = [actor_init_updates, critic_init_updates]
-        self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
+        # Total statistics.
+        combined_stats['total/epochs'] = epoch + 1
+        combined_stats['total/steps'] = t
 
-    def setup_param_noise(self, normalized_obs0):
-        assert self.param_noise is not None
+        for key in sorted(combined_stats.keys()):
+            logger.record_tabular(key, combined_stats[key])
 
-        # Configure perturbed actor.
-        param_noise_actor = copy(self.actor)
-        param_noise_actor.name = 'param_noise_actor'
-        self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
-        logger.info('setting up param noise')
-        self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
+        if rank == 0:
+            logger.dump_tabular()
+        logger.info('')
+        logdir = logger.get_dir()
+        if rank == 0 and logdir:
+            if hasattr(env, 'get_state'):
+                with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
+                    pickle.dump(env.get_state(), f)
+            if eval_env and hasattr(eval_env, 'get_state'):
+                with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
+                    pickle.dump(eval_env.get_state(), f)
 
-        # Configure separate copy for stddev adoption.
-        adaptive_param_noise_actor = copy(self.actor)
-        adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
-        adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
-        self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
-        self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
 
-    def setup_actor_optimizer(self):
-        logger.info('setting up actor optimizer')
-        self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
-        actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
-        actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
-        logger.info('  actor shapes: {}'.format(actor_shapes))
-        logger.info('  actor params: {}'.format(actor_nb_params))
-        self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
-        self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
-            beta1=0.9, beta2=0.999, epsilon=1e-08)
-
-    def setup_critic_optimizer(self):
-        logger.info('setting up critic optimizer')
-        normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
-        self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
-        if self.critic_l2_reg > 0.:
-            critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
-            for var in critic_reg_vars:
-                logger.info('  regularizing: {}'.format(var.name))
-            logger.info('  applying l2 regularization with {}'.format(self.critic_l2_reg))
-            critic_reg = tc.layers.apply_regularization(
-                tc.layers.l2_regularizer(self.critic_l2_reg),
-                weights_list=critic_reg_vars
-            )
-            self.critic_loss += critic_reg
-        critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
-        critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
-        logger.info('  critic shapes: {}'.format(critic_shapes))
-        logger.info('  critic params: {}'.format(critic_nb_params))
-        self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
-        self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
-            beta1=0.9, beta2=0.999, epsilon=1e-08)
-
-    def setup_popart(self):
-        # See https://arxiv.org/pdf/1602.07714.pdf for details.
-        self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
-        new_std = self.ret_rms.std
-        self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
-        new_mean = self.ret_rms.mean
-
-        self.renormalize_Q_outputs_op = []
-        for vs in [self.critic.output_vars, self.target_critic.output_vars]:
-            assert len(vs) == 2
-            M, b = vs
-            assert 'kernel' in M.name
-            assert 'bias' in b.name
-            assert M.get_shape()[-1] == 1
-            assert b.get_shape()[-1] == 1
-            self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
-            self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
-
-    def setup_stats(self):
-        ops = []
-        names = []
-
-        if self.normalize_returns:
-            ops += [self.ret_rms.mean, self.ret_rms.std]
-            names += ['ret_rms_mean', 'ret_rms_std']
-
-        if self.normalize_observations:
-            ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
-            names += ['obs_rms_mean', 'obs_rms_std']
-
-        ops += [tf.reduce_mean(self.critic_tf)]
-        names += ['reference_Q_mean']
-        ops += [reduce_std(self.critic_tf)]
-        names += ['reference_Q_std']
-
-        ops += [tf.reduce_mean(self.critic_with_actor_tf)]
-        names += ['reference_actor_Q_mean']
-        ops += [reduce_std(self.critic_with_actor_tf)]
-        names += ['reference_actor_Q_std']
-
-        ops += [tf.reduce_mean(self.actor_tf)]
-        names += ['reference_action_mean']
-        ops += [reduce_std(self.actor_tf)]
-        names += ['reference_action_std']
-
-        if self.param_noise:
-            ops += [tf.reduce_mean(self.perturbed_actor_tf)]
-            names += ['reference_perturbed_action_mean']
-            ops += [reduce_std(self.perturbed_actor_tf)]
-            names += ['reference_perturbed_action_std']
-
-        self.stats_ops = ops
-        self.stats_names = names
-
-    def pi(self, obs, apply_noise=True, compute_Q=True):
-        if self.param_noise is not None and apply_noise:
-            actor_tf = self.perturbed_actor_tf
-        else:
-            actor_tf = self.actor_tf
-        feed_dict = {self.obs0: [obs]}
-        if compute_Q:
-            action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
-        else:
-            action = self.sess.run(actor_tf, feed_dict=feed_dict)
-            q = None
-        action = action.flatten()
-        if self.action_noise is not None and apply_noise:
-            noise = self.action_noise()
-            assert noise.shape == action.shape
-            action += noise
-        action = np.clip(action, self.action_range[0], self.action_range[1])
-        return action, q
-
-    def store_transition(self, obs0, action, reward, obs1, terminal1):
-        reward *= self.reward_scale
-        self.memory.append(obs0, action, reward, obs1, terminal1)
-        if self.normalize_observations:
-            self.obs_rms.update(np.array([obs0]))
-
-    def train(self):
-        # Get a batch.
-        batch = self.memory.sample(batch_size=self.batch_size)
-
-        if self.normalize_returns and self.enable_popart:
-            old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
-                self.obs1: batch['obs1'],
-                self.rewards: batch['rewards'],
-                self.terminals1: batch['terminals1'].astype('float32'),
-            })
-            self.ret_rms.update(target_Q.flatten())
-            self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
-                self.old_std : np.array([old_std]),
-                self.old_mean : np.array([old_mean]),
-            })
-
-            # Run sanity check. Disabled by default since it slows down things considerably.
-            # print('running sanity check')
-            # target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
-            #     self.obs1: batch['obs1'],
-            #     self.rewards: batch['rewards'],
-            #     self.terminals1: batch['terminals1'].astype('float32'),
-            # })
-            # print(target_Q_new, target_Q, new_mean, new_std)
-            # assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
-        else:
-            target_Q = self.sess.run(self.target_Q, feed_dict={
-                self.obs1: batch['obs1'],
-                self.rewards: batch['rewards'],
-                self.terminals1: batch['terminals1'].astype('float32'),
-            })
-
-        # Get all gradients and perform a synced update.
-        ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
-        actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
-            self.obs0: batch['obs0'],
-            self.actions: batch['actions'],
-            self.critic_target: target_Q,
-        })
-        self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
-        self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
-
-        return critic_loss, actor_loss
-
-    def initialize(self, sess):
-        self.sess = sess
-        self.sess.run(tf.global_variables_initializer())
-        self.actor_optimizer.sync()
-        self.critic_optimizer.sync()
-        self.sess.run(self.target_init_updates)
-
-    def update_target_net(self):
-        self.sess.run(self.target_soft_updates)
-
-    def get_stats(self):
-        if self.stats_sample is None:
-            # Get a sample and keep that fixed for all further computations.
-            # This allows us to estimate the change in value for the same set of inputs.
-            self.stats_sample = self.memory.sample(batch_size=self.batch_size)
-        values = self.sess.run(self.stats_ops, feed_dict={
-            self.obs0: self.stats_sample['obs0'],
-            self.actions: self.stats_sample['actions'],
-        })
-
-        names = self.stats_names[:]
-        assert len(names) == len(values)
-        stats = dict(zip(names, values))
-
-        if self.param_noise is not None:
-            stats = {**stats, **self.param_noise.get_stats()}
-
-        return stats
-
-    def adapt_param_noise(self):
-        if self.param_noise is None:
-            return 0.
-
-        # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
-        batch = self.memory.sample(batch_size=self.batch_size)
-        self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
-            self.param_noise_stddev: self.param_noise.current_stddev,
-        })
-        distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
-            self.obs0: batch['obs0'],
-            self.param_noise_stddev: self.param_noise.current_stddev,
-        })
-
-        mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
-        self.param_noise.adapt(mean_distance)
-        return mean_distance
-
-    def reset(self):
-        # Reset internal state after an episode is complete.
-        if self.action_noise is not None:
-            self.action_noise.reset()
-        if self.param_noise is not None:
-            self.sess.run(self.perturb_policy_ops, feed_dict={
-                self.param_noise_stddev: self.param_noise.current_stddev,
-            })
+    return agent

baselines/ddpg/ddpg_learner.py

@@ -0,0 +1,385 @@
+from copy import copy
+from functools import reduce
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib as tc
+
+from baselines import logger
+from baselines.common.mpi_adam import MpiAdam
+import baselines.common.tf_util as U
+from baselines.common.mpi_running_mean_std import RunningMeanStd
+from mpi4py import MPI
+
+def normalize(x, stats):
+    if stats is None:
+        return x
+    return (x - stats.mean) / stats.std
+
+
+def denormalize(x, stats):
+    if stats is None:
+        return x
+    return x * stats.std + stats.mean
+
+def reduce_std(x, axis=None, keepdims=False):
+    return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims))
+
+def reduce_var(x, axis=None, keepdims=False):
+    m = tf.reduce_mean(x, axis=axis, keepdims=True)
+    devs_squared = tf.square(x - m)
+    return tf.reduce_mean(devs_squared, axis=axis, keepdims=keepdims)
+
+def get_target_updates(vars, target_vars, tau):
+    logger.info('setting up target updates ...')
+    soft_updates = []
+    init_updates = []
+    assert len(vars) == len(target_vars)
+    for var, target_var in zip(vars, target_vars):
+        logger.info('  {} <- {}'.format(target_var.name, var.name))
+        init_updates.append(tf.assign(target_var, var))
+        soft_updates.append(tf.assign(target_var, (1. - tau) * target_var + tau * var))
+    assert len(init_updates) == len(vars)
+    assert len(soft_updates) == len(vars)
+    return tf.group(*init_updates), tf.group(*soft_updates)
+
+
+def get_perturbed_actor_updates(actor, perturbed_actor, param_noise_stddev):
+    assert len(actor.vars) == len(perturbed_actor.vars)
+    assert len(actor.perturbable_vars) == len(perturbed_actor.perturbable_vars)
+
+    updates = []
+    for var, perturbed_var in zip(actor.vars, perturbed_actor.vars):
+        if var in actor.perturbable_vars:
+            logger.info('  {} <- {} + noise'.format(perturbed_var.name, var.name))
+            updates.append(tf.assign(perturbed_var, var + tf.random_normal(tf.shape(var), mean=0., stddev=param_noise_stddev)))
+        else:
+            logger.info('  {} <- {}'.format(perturbed_var.name, var.name))
+            updates.append(tf.assign(perturbed_var, var))
+    assert len(updates) == len(actor.vars)
+    return tf.group(*updates)
+
+
+class DDPG(object):
+    def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
+        gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
+        batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
+        adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
+        critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
+        # Inputs.
+        self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
+        self.obs1 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1')
+        self.terminals1 = tf.placeholder(tf.float32, shape=(None, 1), name='terminals1')
+        self.rewards = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
+        self.actions = tf.placeholder(tf.float32, shape=(None,) + action_shape, name='actions')
+        self.critic_target = tf.placeholder(tf.float32, shape=(None, 1), name='critic_target')
+        self.param_noise_stddev = tf.placeholder(tf.float32, shape=(), name='param_noise_stddev')
+
+        # Parameters.
+        self.gamma = gamma
+        self.tau = tau
+        self.memory = memory
+        self.normalize_observations = normalize_observations
+        self.normalize_returns = normalize_returns
+        self.action_noise = action_noise
+        self.param_noise = param_noise
+        self.action_range = action_range
+        self.return_range = return_range
+        self.observation_range = observation_range
+        self.critic = critic
+        self.actor = actor
+        self.actor_lr = actor_lr
+        self.critic_lr = critic_lr
+        self.clip_norm = clip_norm
+        self.enable_popart = enable_popart
+        self.reward_scale = reward_scale
+        self.batch_size = batch_size
+        self.stats_sample = None
+        self.critic_l2_reg = critic_l2_reg
+
+        # Observation normalization.
+        if self.normalize_observations:
+            with tf.variable_scope('obs_rms'):
+                self.obs_rms = RunningMeanStd(shape=observation_shape)
+        else:
+            self.obs_rms = None
+        normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms),
+            self.observation_range[0], self.observation_range[1])
+        normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms),
+            self.observation_range[0], self.observation_range[1])
+
+        # Return normalization.
+        if self.normalize_returns:
+            with tf.variable_scope('ret_rms'):
+                self.ret_rms = RunningMeanStd()
+        else:
+            self.ret_rms = None
+
+        # Create target networks.
+        target_actor = copy(actor)
+        target_actor.name = 'target_actor'
+        self.target_actor = target_actor
+        target_critic = copy(critic)
+        target_critic.name = 'target_critic'
+        self.target_critic = target_critic
+
+        # Create networks and core TF parts that are shared across setup parts.
+        self.actor_tf = actor(normalized_obs0)
+        self.normalized_critic_tf = critic(normalized_obs0, self.actions)
+        self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+        self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True)
+        self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms)
+        Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms)
+        self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1
+
+        # Set up parts.
+        if self.param_noise is not None:
+            self.setup_param_noise(normalized_obs0)
+        self.setup_actor_optimizer()
+        self.setup_critic_optimizer()
+        if self.normalize_returns and self.enable_popart:
+            self.setup_popart()
+        self.setup_stats()
+        self.setup_target_network_updates()
+
+        self.initial_state = None # recurrent architectures not supported yet
+
+    def setup_target_network_updates(self):
+        actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau)
+        critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau)
+        self.target_init_updates = [actor_init_updates, critic_init_updates]
+        self.target_soft_updates = [actor_soft_updates, critic_soft_updates]
+
+    def setup_param_noise(self, normalized_obs0):
+        assert self.param_noise is not None
+
+        # Configure perturbed actor.
+        param_noise_actor = copy(self.actor)
+        param_noise_actor.name = 'param_noise_actor'
+        self.perturbed_actor_tf = param_noise_actor(normalized_obs0)
+        logger.info('setting up param noise')
+        self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev)
+
+        # Configure separate copy for stddev adoption.
+        adaptive_param_noise_actor = copy(self.actor)
+        adaptive_param_noise_actor.name = 'adaptive_param_noise_actor'
+        adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0)
+        self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev)
+        self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(tf.square(self.actor_tf - adaptive_actor_tf)))
+
+    def setup_actor_optimizer(self):
+        logger.info('setting up actor optimizer')
+        self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf)
+        actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars]
+        actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
+        logger.info('  actor shapes: {}'.format(actor_shapes))
+        logger.info('  actor params: {}'.format(actor_nb_params))
+        self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm)
+        self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars,
+            beta1=0.9, beta2=0.999, epsilon=1e-08)
+
+    def setup_critic_optimizer(self):
+        logger.info('setting up critic optimizer')
+        normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
+        self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
+        if self.critic_l2_reg > 0.:
+            critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
+            for var in critic_reg_vars:
+                logger.info('  regularizing: {}'.format(var.name))
+            logger.info('  applying l2 regularization with {}'.format(self.critic_l2_reg))
+            critic_reg = tc.layers.apply_regularization(
+                tc.layers.l2_regularizer(self.critic_l2_reg),
+                weights_list=critic_reg_vars
+            )
+            self.critic_loss += critic_reg
+        critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars]
+        critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes])
+        logger.info('  critic shapes: {}'.format(critic_shapes))
+        logger.info('  critic params: {}'.format(critic_nb_params))
+        self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm)
+        self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars,
+            beta1=0.9, beta2=0.999, epsilon=1e-08)
+
+    def setup_popart(self):
+        # See https://arxiv.org/pdf/1602.07714.pdf for details.
+        self.old_std = tf.placeholder(tf.float32, shape=[1], name='old_std')
+        new_std = self.ret_rms.std
+        self.old_mean = tf.placeholder(tf.float32, shape=[1], name='old_mean')
+        new_mean = self.ret_rms.mean
+
+        self.renormalize_Q_outputs_op = []
+        for vs in [self.critic.output_vars, self.target_critic.output_vars]:
+            assert len(vs) == 2
+            M, b = vs
+            assert 'kernel' in M.name
+            assert 'bias' in b.name
+            assert M.get_shape()[-1] == 1
+            assert b.get_shape()[-1] == 1
+            self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)]
+            self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)]
+
+    def setup_stats(self):
+        ops = []
+        names = []
+
+        if self.normalize_returns:
+            ops += [self.ret_rms.mean, self.ret_rms.std]
+            names += ['ret_rms_mean', 'ret_rms_std']
+
+        if self.normalize_observations:
+            ops += [tf.reduce_mean(self.obs_rms.mean), tf.reduce_mean(self.obs_rms.std)]
+            names += ['obs_rms_mean', 'obs_rms_std']
+
+        ops += [tf.reduce_mean(self.critic_tf)]
+        names += ['reference_Q_mean']
+        ops += [reduce_std(self.critic_tf)]
+        names += ['reference_Q_std']
+
+        ops += [tf.reduce_mean(self.critic_with_actor_tf)]
+        names += ['reference_actor_Q_mean']
+        ops += [reduce_std(self.critic_with_actor_tf)]
+        names += ['reference_actor_Q_std']
+
+        ops += [tf.reduce_mean(self.actor_tf)]
+        names += ['reference_action_mean']
+        ops += [reduce_std(self.actor_tf)]
+        names += ['reference_action_std']
+
+        if self.param_noise:
+            ops += [tf.reduce_mean(self.perturbed_actor_tf)]
+            names += ['reference_perturbed_action_mean']
+            ops += [reduce_std(self.perturbed_actor_tf)]
+            names += ['reference_perturbed_action_std']
+
+        self.stats_ops = ops
+        self.stats_names = names
+
+    def step(self, obs, apply_noise=True, compute_Q=True):
+        if self.param_noise is not None and apply_noise:
+            actor_tf = self.perturbed_actor_tf
+        else:
+            actor_tf = self.actor_tf
+        feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
+        if compute_Q:
+            action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
+        else:
+            action = self.sess.run(actor_tf, feed_dict=feed_dict)
+            q = None
+
+        if self.action_noise is not None and apply_noise:
+            noise = self.action_noise()
+            assert noise.shape == action.shape
+            action += noise
+        action = np.clip(action, self.action_range[0], self.action_range[1])
+
+
+        return action, q, None, None
+
+    def store_transition(self, obs0, action, reward, obs1, terminal1):
+        reward *= self.reward_scale
+
+        B = obs0.shape[0]
+        for b in range(B):
+            self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b])
+            if self.normalize_observations:
+                self.obs_rms.update(np.array([obs0[b]]))
+
+    def train(self):
+        # Get a batch.
+        batch = self.memory.sample(batch_size=self.batch_size)
+
+        if self.normalize_returns and self.enable_popart:
+            old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={
+                self.obs1: batch['obs1'],
+                self.rewards: batch['rewards'],
+                self.terminals1: batch['terminals1'].astype('float32'),
+            })
+            self.ret_rms.update(target_Q.flatten())
+            self.sess.run(self.renormalize_Q_outputs_op, feed_dict={
+                self.old_std : np.array([old_std]),
+                self.old_mean : np.array([old_mean]),
+            })
+
+            # Run sanity check. Disabled by default since it slows down things considerably.
+            # print('running sanity check')
+            # target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={
+            #     self.obs1: batch['obs1'],
+            #     self.rewards: batch['rewards'],
+            #     self.terminals1: batch['terminals1'].astype('float32'),
+            # })
+            # print(target_Q_new, target_Q, new_mean, new_std)
+            # assert (np.abs(target_Q - target_Q_new) < 1e-3).all()
+        else:
+            target_Q = self.sess.run(self.target_Q, feed_dict={
+                self.obs1: batch['obs1'],
+                self.rewards: batch['rewards'],
+                self.terminals1: batch['terminals1'].astype('float32'),
+            })
+
+        # Get all gradients and perform a synced update.
+        ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss]
+        actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={
+            self.obs0: batch['obs0'],
+            self.actions: batch['actions'],
+            self.critic_target: target_Q,
+        })
+        self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr)
+        self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr)
+
+        return critic_loss, actor_loss
+
+    def initialize(self, sess):
+        self.sess = sess
+        self.sess.run(tf.global_variables_initializer())
+        self.actor_optimizer.sync()
+        self.critic_optimizer.sync()
+        self.sess.run(self.target_init_updates)
+
+    def update_target_net(self):
+        self.sess.run(self.target_soft_updates)
+
+    def get_stats(self):
+        if self.stats_sample is None:
+            # Get a sample and keep that fixed for all further computations.
+            # This allows us to estimate the change in value for the same set of inputs.
+            self.stats_sample = self.memory.sample(batch_size=self.batch_size)
+        values = self.sess.run(self.stats_ops, feed_dict={
+            self.obs0: self.stats_sample['obs0'],
+            self.actions: self.stats_sample['actions'],
+        })
+
+        names = self.stats_names[:]
+        assert len(names) == len(values)
+        stats = dict(zip(names, values))
+
+        if self.param_noise is not None:
+            stats = {**stats, **self.param_noise.get_stats()}
+
+        return stats
+
+    def adapt_param_noise(self):
+        if self.param_noise is None:
+            return 0.
+
+        # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation.
+        batch = self.memory.sample(batch_size=self.batch_size)
+        self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={
+            self.param_noise_stddev: self.param_noise.current_stddev,
+        })
+        distance = self.sess.run(self.adaptive_policy_distance, feed_dict={
+            self.obs0: batch['obs0'],
+            self.param_noise_stddev: self.param_noise.current_stddev,
+        })
+
+        mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+        self.param_noise.adapt(mean_distance)
+        return mean_distance
+
+    def reset(self):
+        # Reset internal state after an episode is complete.
+        if self.action_noise is not None:
+            self.action_noise.reset()
+        if self.param_noise is not None:
+            self.sess.run(self.perturb_policy_ops, feed_dict={
+                self.param_noise_stddev: self.param_noise.current_stddev,
+            })

baselines/ddpg/main.py

@@ -1,123 +0,0 @@
-import argparse
-import time
-import os
-import logging
-from baselines import logger, bench
-from baselines.common.misc_util import (
-    set_global_seeds,
-    boolean_flag,
-)
-import baselines.ddpg.training as training
-from baselines.ddpg.models import Actor, Critic
-from baselines.ddpg.memory import Memory
-from baselines.ddpg.noise import *
-
-import gym
-import tensorflow as tf
-from mpi4py import MPI
-
-def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
-    # Configure things.
-    rank = MPI.COMM_WORLD.Get_rank()
-    if rank != 0:
-        logger.set_level(logger.DISABLED)
-
-    # Create envs.
-    env = gym.make(env_id)
-    env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
-
-    if evaluation and rank==0:
-        eval_env = gym.make(env_id)
-        eval_env = bench.Monitor(eval_env, os.path.join(logger.get_dir(), 'gym_eval'))
-        env = bench.Monitor(env, None)
-    else:
-        eval_env = None
-
-    # Parse noise_type
-    action_noise = None
-    param_noise = None
-    nb_actions = env.action_space.shape[-1]
-    for current_noise_type in noise_type.split(','):
-        current_noise_type = current_noise_type.strip()
-        if current_noise_type == 'none':
-            pass
-        elif 'adaptive-param' in current_noise_type:
-            _, stddev = current_noise_type.split('_')
-            param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
-        elif 'normal' in current_noise_type:
-            _, stddev = current_noise_type.split('_')
-            action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
-        elif 'ou' in current_noise_type:
-            _, stddev = current_noise_type.split('_')
-            action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
-        else:
-            raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
-
-    # Configure components.
-    memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
-    critic = Critic(layer_norm=layer_norm)
-    actor = Actor(nb_actions, layer_norm=layer_norm)
-
-    # Seed everything to make things reproducible.
-    seed = seed + 1000000 * rank
-    logger.info('rank {}: seed={}, logdir={}'.format(rank, seed, logger.get_dir()))
-    tf.reset_default_graph()
-    set_global_seeds(seed)
-    env.seed(seed)
-    if eval_env is not None:
-        eval_env.seed(seed)
-
-    # Disable logging for rank != 0 to avoid noise.
-    if rank == 0:
-        start_time = time.time()
-    training.train(env=env, eval_env=eval_env, param_noise=param_noise,
-        action_noise=action_noise, actor=actor, critic=critic, memory=memory, **kwargs)
-    env.close()
-    if eval_env is not None:
-        eval_env.close()
-    if rank == 0:
-        logger.info('total runtime: {}s'.format(time.time() - start_time))
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    parser.add_argument('--env-id', type=str, default='HalfCheetah-v1')
-    boolean_flag(parser, 'render-eval', default=False)
-    boolean_flag(parser, 'layer-norm', default=True)
-    boolean_flag(parser, 'render', default=False)
-    boolean_flag(parser, 'normalize-returns', default=False)
-    boolean_flag(parser, 'normalize-observations', default=True)
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
-    parser.add_argument('--critic-l2-reg', type=float, default=1e-2)
-    parser.add_argument('--batch-size', type=int, default=64)  # per MPI worker
-    parser.add_argument('--actor-lr', type=float, default=1e-4)
-    parser.add_argument('--critic-lr', type=float, default=1e-3)
-    boolean_flag(parser, 'popart', default=False)
-    parser.add_argument('--gamma', type=float, default=0.99)
-    parser.add_argument('--reward-scale', type=float, default=1.)
-    parser.add_argument('--clip-norm', type=float, default=None)
-    parser.add_argument('--nb-epochs', type=int, default=500)  # with default settings, perform 1M steps total
-    parser.add_argument('--nb-epoch-cycles', type=int, default=20)
-    parser.add_argument('--nb-train-steps', type=int, default=50)  # per epoch cycle and MPI worker
-    parser.add_argument('--nb-eval-steps', type=int, default=100)  # per epoch cycle and MPI worker
-    parser.add_argument('--nb-rollout-steps', type=int, default=100)  # per epoch cycle and MPI worker
-    parser.add_argument('--noise-type', type=str, default='adaptive-param_0.2')  # choices are adaptive-param_xx, ou_xx, normal_xx, none
-    parser.add_argument('--num-timesteps', type=int, default=None)
-    boolean_flag(parser, 'evaluation', default=False)
-    args = parser.parse_args()
-    # we don't directly specify timesteps for this script, so make sure that if we do specify them
-    # they agree with the other parameters
-    if args.num_timesteps is not None:
-        assert(args.num_timesteps == args.nb_epochs * args.nb_epoch_cycles * args.nb_rollout_steps)
-    dict_args = vars(args)
-    del dict_args['num_timesteps']
-    return dict_args
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    if MPI.COMM_WORLD.Get_rank() == 0:
-        logger.configure()
-    # Run actual script.
-    run(**args)

baselines/ddpg/memory.py

@@ -51,7 +51,7 @@ class Memory(object):
 
     def sample(self, batch_size):
         # Draw such that we always have a proceeding element.
-        batch_idxs = np.random.random_integers(self.nb_entries - 2, size=batch_size)
+        batch_idxs = np.random.randint(self.nb_entries - 2, size=batch_size)
 
         obs0_batch = self.observations0.get_batch(batch_idxs)
         obs1_batch = self.observations1.get_batch(batch_idxs)
@@ -71,7 +71,7 @@ class Memory(object):
     def append(self, obs0, action, reward, obs1, terminal1, training=True):
         if not training:
             return
-        
+
         self.observations0.append(obs0)
         self.actions.append(action)
         self.rewards.append(reward)

baselines/ddpg/models.py

@@ -1,10 +1,11 @@
 import tensorflow as tf
-import tensorflow.contrib as tc
+from baselines.common.models import get_network_builder
 
 
 class Model(object):
-    def __init__(self, name):
+    def __init__(self, name, network='mlp', **network_kwargs):
         self.name = name
+        self.network_builder = get_network_builder(network)(**network_kwargs)
 
     @property
     def vars(self):
@@ -20,54 +21,27 @@ class Model(object):
 
 
 class Actor(Model):
-    def __init__(self, nb_actions, name='actor', layer_norm=True):
-        super(Actor, self).__init__(name=name)
+    def __init__(self, nb_actions, name='actor', network='mlp', **network_kwargs):
+        super().__init__(name=name, network=network, **network_kwargs)
         self.nb_actions = nb_actions
-        self.layer_norm = layer_norm
 
     def __call__(self, obs, reuse=False):
-        with tf.variable_scope(self.name) as scope:
-            if reuse:
-                scope.reuse_variables()
-
-            x = obs
-            x = tf.layers.dense(x, 64)
-            if self.layer_norm:
-                x = tc.layers.layer_norm(x, center=True, scale=True)
-            x = tf.nn.relu(x)
-            
-            x = tf.layers.dense(x, 64)
-            if self.layer_norm:
-                x = tc.layers.layer_norm(x, center=True, scale=True)
-            x = tf.nn.relu(x)
-            
+        with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
+            x = self.network_builder(obs)
             x = tf.layers.dense(x, self.nb_actions, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
             x = tf.nn.tanh(x)
         return x
 
 
 class Critic(Model):
-    def __init__(self, name='critic', layer_norm=True):
-        super(Critic, self).__init__(name=name)
-        self.layer_norm = layer_norm
+    def __init__(self, name='critic', network='mlp', **network_kwargs):
+        super().__init__(name=name, network=network, **network_kwargs)
+        self.layer_norm = True
 
     def __call__(self, obs, action, reuse=False):
-        with tf.variable_scope(self.name) as scope:
-            if reuse:
-                scope.reuse_variables()
-
-            x = obs
-            x = tf.layers.dense(x, 64)
-            if self.layer_norm:
-                x = tc.layers.layer_norm(x, center=True, scale=True)
-            x = tf.nn.relu(x)
-
-            x = tf.concat([x, action], axis=-1)
-            x = tf.layers.dense(x, 64)
-            if self.layer_norm:
-                x = tc.layers.layer_norm(x, center=True, scale=True)
-            x = tf.nn.relu(x)
-
+        with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
+            x = tf.concat([obs, action], axis=-1) # this assumes observation and action can be concatenated
+            x = self.network_builder(x)
             x = tf.layers.dense(x, 1, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
         return x
 

baselines/ddpg/training.py

@@ -1,191 +0,0 @@
-import os
-import time
-from collections import deque
-import pickle
-
-from baselines.ddpg.ddpg import DDPG
-import baselines.common.tf_util as U
-
-from baselines import logger
-import numpy as np
-import tensorflow as tf
-from mpi4py import MPI
-
-
-def train(env, nb_epochs, nb_epoch_cycles, render_eval, reward_scale, render, param_noise, actor, critic,
-    normalize_returns, normalize_observations, critic_l2_reg, actor_lr, critic_lr, action_noise,
-    popart, gamma, clip_norm, nb_train_steps, nb_rollout_steps, nb_eval_steps, batch_size, memory,
-    tau=0.01, eval_env=None, param_noise_adaption_interval=50):
-    rank = MPI.COMM_WORLD.Get_rank()
-
-    assert (np.abs(env.action_space.low) == env.action_space.high).all()  # we assume symmetric actions.
-    max_action = env.action_space.high
-    logger.info('scaling actions by {} before executing in env'.format(max_action))
-    agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
-        gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,
-        batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,
-        actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm,
-        reward_scale=reward_scale)
-    logger.info('Using agent with the following configuration:')
-    logger.info(str(agent.__dict__.items()))
-
-    # Set up logging stuff only for a single worker.
-    if rank == 0:
-        saver = tf.train.Saver()
-    else:
-        saver = None
-
-    step = 0
-    episode = 0
-    eval_episode_rewards_history = deque(maxlen=100)
-    episode_rewards_history = deque(maxlen=100)
-    with U.single_threaded_session() as sess:
-        # Prepare everything.
-        agent.initialize(sess)
-        sess.graph.finalize()
-
-        agent.reset()
-        obs = env.reset()
-        if eval_env is not None:
-            eval_obs = eval_env.reset()
-        done = False
-        episode_reward = 0.
-        episode_step = 0
-        episodes = 0
-        t = 0
-
-        epoch = 0
-        start_time = time.time()
-
-        epoch_episode_rewards = []
-        epoch_episode_steps = []
-        epoch_episode_eval_rewards = []
-        epoch_episode_eval_steps = []
-        epoch_start_time = time.time()
-        epoch_actions = []
-        epoch_qs = []
-        epoch_episodes = 0
-        for epoch in range(nb_epochs):
-            for cycle in range(nb_epoch_cycles):
-                # Perform rollouts.
-                for t_rollout in range(nb_rollout_steps):
-                    # Predict next action.
-                    action, q = agent.pi(obs, apply_noise=True, compute_Q=True)
-                    assert action.shape == env.action_space.shape
-
-                    # Execute next action.
-                    if rank == 0 and render:
-                        env.render()
-                    assert max_action.shape == action.shape
-                    new_obs, r, done, info = env.step(max_action * action)  # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
-                    t += 1
-                    if rank == 0 and render:
-                        env.render()
-                    episode_reward += r
-                    episode_step += 1
-
-                    # Book-keeping.
-                    epoch_actions.append(action)
-                    epoch_qs.append(q)
-                    agent.store_transition(obs, action, r, new_obs, done)
-                    obs = new_obs
-
-                    if done:
-                        # Episode done.
-                        epoch_episode_rewards.append(episode_reward)
-                        episode_rewards_history.append(episode_reward)
-                        epoch_episode_steps.append(episode_step)
-                        episode_reward = 0.
-                        episode_step = 0
-                        epoch_episodes += 1
-                        episodes += 1
-
-                        agent.reset()
-                        obs = env.reset()
-
-                # Train.
-                epoch_actor_losses = []
-                epoch_critic_losses = []
-                epoch_adaptive_distances = []
-                for t_train in range(nb_train_steps):
-                    # Adapt param noise, if necessary.
-                    if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
-                        distance = agent.adapt_param_noise()
-                        epoch_adaptive_distances.append(distance)
-
-                    cl, al = agent.train()
-                    epoch_critic_losses.append(cl)
-                    epoch_actor_losses.append(al)
-                    agent.update_target_net()
-
-                # Evaluate.
-                eval_episode_rewards = []
-                eval_qs = []
-                if eval_env is not None:
-                    eval_episode_reward = 0.
-                    for t_rollout in range(nb_eval_steps):
-                        eval_action, eval_q = agent.pi(eval_obs, apply_noise=False, compute_Q=True)
-                        eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action)  # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
-                        if render_eval:
-                            eval_env.render()
-                        eval_episode_reward += eval_r
-
-                        eval_qs.append(eval_q)
-                        if eval_done:
-                            eval_obs = eval_env.reset()
-                            eval_episode_rewards.append(eval_episode_reward)
-                            eval_episode_rewards_history.append(eval_episode_reward)
-                            eval_episode_reward = 0.
-
-            mpi_size = MPI.COMM_WORLD.Get_size()
-            # Log stats.
-            # XXX shouldn't call np.mean on variable length lists
-            duration = time.time() - start_time
-            stats = agent.get_stats()
-            combined_stats = stats.copy()
-            combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
-            combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
-            combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
-            combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
-            combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
-            combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
-            combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
-            combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
-            combined_stats['total/duration'] = duration
-            combined_stats['total/steps_per_second'] = float(t) / float(duration)
-            combined_stats['total/episodes'] = episodes
-            combined_stats['rollout/episodes'] = epoch_episodes
-            combined_stats['rollout/actions_std'] = np.std(epoch_actions)
-            # Evaluation statistics.
-            if eval_env is not None:
-                combined_stats['eval/return'] = eval_episode_rewards
-                combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
-                combined_stats['eval/Q'] = eval_qs
-                combined_stats['eval/episodes'] = len(eval_episode_rewards)
-            def as_scalar(x):
-                if isinstance(x, np.ndarray):
-                    assert x.size == 1
-                    return x[0]
-                elif np.isscalar(x):
-                    return x
-                else:
-                    raise ValueError('expected scalar, got %s'%x)
-            combined_stats_sums = MPI.COMM_WORLD.allreduce(np.array([as_scalar(x) for x in combined_stats.values()]))
-            combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
-
-            # Total statistics.
-            combined_stats['total/epochs'] = epoch + 1
-            combined_stats['total/steps'] = t
-
-            for key in sorted(combined_stats.keys()):
-                logger.record_tabular(key, combined_stats[key])
-            logger.dump_tabular()
-            logger.info('')
-            logdir = logger.get_dir()
-            if rank == 0 and logdir:
-                if hasattr(env, 'get_state'):
-                    with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
-                        pickle.dump(env.get_state(), f)
-                if eval_env and hasattr(eval_env, 'get_state'):
-                    with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
-                        pickle.dump(eval_env.get_state(), f)

baselines/deepq/README.md

@@ -9,44 +9,29 @@ Here's a list of commands to run to quickly get a working example:
 
 ```bash
 # Train model and save the results to cartpole_model.pkl
-python -m baselines.deepq.experiments.train_cartpole
+python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5
 # Load the model saved in cartpole_model.pkl and visualize the learned policy
-python -m baselines.deepq.experiments.enjoy_cartpole
+python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play
 ```
 
-
-Be sure to check out the source code of [both](experiments/train_cartpole.py) [files](experiments/enjoy_cartpole.py)!
-
 ## If you wish to apply DQN to solve a problem.
 
 Check out our simple agent trained with one stop shop `deepq.learn` function. 
 
 - [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent.
-- [baselines/deepq/experiments/train_pong.py](experiments/train_pong.py) - train a Pong agent using convolutional neural networks.
 
-In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. For both of the files listed above there are complimentary files `enjoy_cartpole.py` and `enjoy_pong.py` respectively, that load and visualize the learned policy.
+In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. Complimentary file `enjoy_cartpole.py` loads and visualizes the learned policy.
 
 ## If you wish to experiment with the algorithm
 
 ##### Check out the examples
 
-
 - [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm.
-- [baselines/deepq/experiments/atari/train.py](experiments/atari/train.py) - more robust setup for training at scale.
-
-
-##### Download a pretrained Atari agent
-
-For some research projects it is sometimes useful to have an already trained agent handy. There's a variety of models to choose from. You can list them all by running:
+- [baselines/deepq/defaults.py](defaults.py) - settings for training on atari. Run 
 
 ```bash
-python -m baselines.deepq.experiments.atari.download_model
+python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 
 ```
+to train on Atari Pong (see more in repo-wide [README.md](../../README.md#training-models))
 
-Once you pick a model, you can download it and visualize the learned policy. Be sure to pass `--dueling` flag to visualization script when using dueling models.
 
-```bash
-python -m baselines.deepq.experiments.atari.download_model --blob model-atari-duel-pong-1 --model-dir /tmp/models
-python -m baselines.deepq.experiments.atari.enjoy --model-dir /tmp/models/model-atari-duel-pong-1 --env Pong --dueling
-
-```

baselines/deepq/__init__.py

@@ -1,8 +1,8 @@
 from baselines.deepq import models  # noqa
 from baselines.deepq.build_graph import build_act, build_train  # noqa
-from baselines.deepq.simple import learn, load  # noqa
+from baselines.deepq.deepq import learn, load_act  # noqa
 from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer  # noqa
 
 def wrap_atari_dqn(env):
     from baselines.common.atari_wrappers import wrap_deepmind
-    return wrap_deepmind(env, frame_stack=True, scale=True)
+    return wrap_deepmind(env, frame_stack=True, scale=True)

baselines/deepq/build_graph.py

@@ -309,7 +309,7 @@ def build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope="deepq",
                          outputs=output_actions,
                          givens={update_eps_ph: -1.0, stochastic_ph: True, reset_ph: False, update_param_noise_threshold_ph: False, update_param_noise_scale_ph: False},
                          updates=updates)
-        def act(ob, reset, update_param_noise_threshold, update_param_noise_scale, stochastic=True, update_eps=-1):
+        def act(ob, reset=False, update_param_noise_threshold=False, update_param_noise_scale=False, stochastic=True, update_eps=-1):
             return _act(ob, stochastic, update_eps, reset, update_param_noise_threshold, update_param_noise_scale)
         return act
 

baselines/deepq/deepq.py

@@ -7,23 +7,28 @@ import cloudpickle
 import numpy as np
 
 import baselines.common.tf_util as U
-from baselines.common.tf_util import load_state, save_state
-from baselines import logger
+from baselines.common.tf_util import load_variables, save_variables
+from baselines import logger, registry
 from baselines.common.schedules import LinearSchedule
-from baselines.common.input import observation_input
+from baselines.common import set_global_seeds
 
 from baselines import deepq
 from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer
 from baselines.deepq.utils import ObservationInput
 
+from baselines.common.tf_util import get_session
+from baselines.deepq.models import build_q_func
+from baselines.deepq.defaults import defaults
+
 
 class ActWrapper(object):
     def __init__(self, act, act_params):
         self._act = act
         self._act_params = act_params
+        self.initial_state = None
 
     @staticmethod
-    def load(path):
+    def load_act(path):
         with open(path, "rb") as f:
             model_data, act_params = cloudpickle.load(f)
         act = deepq.build_act(**act_params)
@@ -35,20 +40,26 @@ class ActWrapper(object):
                 f.write(model_data)
 
             zipfile.ZipFile(arc_path, 'r', zipfile.ZIP_DEFLATED).extractall(td)
-            load_state(os.path.join(td, "model"))
+            load_variables(os.path.join(td, "model"))
 
         return ActWrapper(act, act_params)
 
     def __call__(self, *args, **kwargs):
         return self._act(*args, **kwargs)
 
-    def save(self, path=None):
+    def step(self, observation, **kwargs):
+        # DQN doesn't use RNNs so we ignore states and masks
+        kwargs.pop('S', None)
+        kwargs.pop('M', None)
+        return self._act([observation], **kwargs), None, None, None
+
+    def save_act(self, path=None):
         """Save model to a pickle located at `path`"""
         if path is None:
             path = os.path.join(logger.get_dir(), "model.pkl")
 
         with tempfile.TemporaryDirectory() as td:
-            save_state(os.path.join(td, "model"))
+            save_variables(os.path.join(td, "model"))
             arc_name = os.path.join(td, "packed.zip")
             with zipfile.ZipFile(arc_name, 'w') as zipf:
                 for root, dirs, files in os.walk(td):
@@ -61,8 +72,11 @@ class ActWrapper(object):
         with open(path, "wb") as f:
             cloudpickle.dump((model_data, self._act_params), f)
 
+    def save(self, path):
+        save_variables(path)
 
-def load(path):
+
+def load_act(path):
     """Load act function that was returned by learn function.
 
     Parameters
@@ -76,13 +90,15 @@ def load(path):
         function that takes a batch of observations
         and returns actions.
     """
-    return ActWrapper.load(path)
+    return ActWrapper.load_act(path)
 
 
+@registry.register('deepq', supports_vecenv=False, defaults=defaults)
 def learn(env,
-          q_func,
+          network,
+          seed=None,
           lr=5e-4,
-          max_timesteps=100000,
+          total_timesteps=100000,
           buffer_size=50000,
           exploration_fraction=0.1,
           exploration_final_eps=0.02,
@@ -100,26 +116,25 @@ def learn(env,
           prioritized_replay_beta_iters=None,
           prioritized_replay_eps=1e-6,
           param_noise=False,
-          callback=None):
+          callback=None,
+          load_path=None,
+          **network_kwargs
+            ):
     """Train a deepq model.
 
     Parameters
     -------
     env: gym.Env
         environment to train on
-    q_func: (tf.Variable, int, str, bool) -> tf.Variable
-        the model that takes the following inputs:
-            observation_in: object
-                the output of observation placeholder
-            num_actions: int
-                number of actions
-            scope: str
-            reuse: bool
-                should be passed to outer variable scope
-        and returns a tensor of shape (batch_size, num_actions) with values of every action.
+    network: string or a function
+        neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
+        (mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
+        will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
+    seed: int or None
+        prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
     lr: float
         learning rate for adam optimizer
-    max_timesteps: int
+    total_timesteps: int
         number of env steps to optimizer for
     buffer_size: int
         size of the replay buffer
@@ -153,12 +168,16 @@ def learn(env,
         initial value of beta for prioritized replay buffer
     prioritized_replay_beta_iters: int
         number of iterations over which beta will be annealed from initial value
-        to 1.0. If set to None equals to max_timesteps.
+        to 1.0. If set to None equals to total_timesteps.
     prioritized_replay_eps: float
         epsilon to add to the TD errors when updating priorities.
     callback: (locals, globals) -> None
         function called at every steps with state of the algorithm.
         If callback returns true training stops.
+    load_path: str
+        path to load the model from. (default: None)
+    **network_kwargs
+        additional keyword arguments to pass to the network builder.
 
     Returns
     -------
@@ -168,14 +187,17 @@ def learn(env,
     """
     # Create all the functions necessary to train the model
 
-    sess = tf.Session()
-    sess.__enter__()
+    sess = get_session()
+    set_global_seeds(seed)
+
+    q_func = build_q_func(network, **network_kwargs)
 
     # capture the shape outside the closure so that the env object is not serialized
     # by cloudpickle when serializing make_obs_ph
 
+    observation_space = env.observation_space
     def make_obs_ph(name):
-        return ObservationInput(env.observation_space, name=name)
+        return ObservationInput(observation_space, name=name)
 
     act, train, update_target, debug = deepq.build_train(
         make_obs_ph=make_obs_ph,
@@ -199,7 +221,7 @@ def learn(env,
     if prioritized_replay:
         replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
         if prioritized_replay_beta_iters is None:
-            prioritized_replay_beta_iters = max_timesteps
+            prioritized_replay_beta_iters = total_timesteps
         beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
                                        initial_p=prioritized_replay_beta0,
                                        final_p=1.0)
@@ -207,7 +229,7 @@ def learn(env,
         replay_buffer = ReplayBuffer(buffer_size)
         beta_schedule = None
     # Create the schedule for exploration starting from 1.
-    exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * max_timesteps),
+    exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
                                  initial_p=1.0,
                                  final_p=exploration_final_eps)
 
@@ -225,12 +247,17 @@ def learn(env,
 
         model_file = os.path.join(td, "model")
         model_saved = False
+
         if tf.train.latest_checkpoint(td) is not None:
-            load_state(model_file)
+            load_variables(model_file)
             logger.log('Loaded model from {}'.format(model_file))
             model_saved = True
+        elif load_path is not None:
+            load_variables(load_path)
+            logger.log('Loaded model from {}'.format(load_path))
 
-        for t in range(max_timesteps):
+
+        for t in range(total_timesteps):
             if callback is not None:
                 if callback(locals(), globals()):
                     break
@@ -295,12 +322,12 @@ def learn(env,
                     if print_freq is not None:
                         logger.log("Saving model due to mean reward increase: {} -> {}".format(
                                    saved_mean_reward, mean_100ep_reward))
-                    save_state(model_file)
+                    save_variables(model_file)
                     model_saved = True
                     saved_mean_reward = mean_100ep_reward
         if model_saved:
             if print_freq is not None:
                 logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
-            load_state(model_file)
+            load_variables(model_file)
 
     return act

baselines/deepq/defaults.py

@@ -0,0 +1,23 @@
+def atari():
+    return dict(
+        network='conv_only',
+        lr=1e-4,
+        buffer_size=10000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.01,
+        train_freq=4,
+        learning_starts=10000,
+        target_network_update_freq=1000,
+        gamma=0.99,
+        prioritized_replay=True,
+        prioritized_replay_alpha=0.6,
+        checkpoint_freq=10000,
+        checkpoint_path=None,
+        dueling=True
+    )
+
+
+defaults = {
+    'atari': atari(),
+    'retro': atari()
+}

baselines/deepq/experiments/enjoy_cartpole.py

@@ -5,7 +5,7 @@ from baselines import deepq
 
 def main():
     env = gym.make("CartPole-v0")
-    act = deepq.load("cartpole_model.pkl")
+    act = deepq.learn(env, network='mlp', total_timesteps=0, load_path="cartpole_model.pkl")
 
     while True:
         obs, done = env.reset(), False

baselines/deepq/experiments/enjoy_mountaincar.py

@@ -1,11 +1,17 @@
 import gym
 
 from baselines import deepq
+from baselines.common import models
 
 
 def main():
     env = gym.make("MountainCar-v0")
-    act = deepq.load("mountaincar_model.pkl")
+    act = deepq.learn(
+        env,
+        network=models.mlp(num_layers=1, num_hidden=64),
+        total_timesteps=0,
+        load_path='mountaincar_model.pkl'
+    )
 
     while True:
         obs, done = env.reset(), False

baselines/deepq/experiments/enjoy_pong.py

@@ -5,14 +5,21 @@ from baselines import deepq
 def main():
     env = gym.make("PongNoFrameskip-v4")
     env = deepq.wrap_atari_dqn(env)
-    act = deepq.load("pong_model.pkl")
+    model = deepq.learn(
+        env,
+        "conv_only",
+        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+        hiddens=[256],
+        dueling=True,
+        total_timesteps=0
+    )
 
     while True:
         obs, done = env.reset(), False
         episode_rew = 0
         while not done:
             env.render()
-            obs, rew, done, _ = env.step(act(obs[None])[0])
+            obs, rew, done, _ = env.step(model(obs[None])[0])
             episode_rew += rew
         print("Episode reward", episode_rew)
 

baselines/deepq/experiments/run_atari.py

@@ -1,54 +0,0 @@
-from baselines import deepq
-from baselines.common import set_global_seeds
-from baselines import bench
-import argparse
-from baselines import logger
-from baselines.common.atari_wrappers import make_atari
-
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
-    parser.add_argument('--prioritized', type=int, default=1)
-    parser.add_argument('--prioritized-replay-alpha', type=float, default=0.6)
-    parser.add_argument('--dueling', type=int, default=1)
-    parser.add_argument('--num-timesteps', type=int, default=int(10e6))
-    parser.add_argument('--checkpoint-freq', type=int, default=10000)
-    parser.add_argument('--checkpoint-path', type=str, default=None)
-
-    args = parser.parse_args()
-    logger.configure()
-    set_global_seeds(args.seed)
-    env = make_atari(args.env)
-    env = bench.Monitor(env, logger.get_dir())
-    env = deepq.wrap_atari_dqn(env)
-    model = deepq.models.cnn_to_mlp(
-        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
-        hiddens=[256],
-        dueling=bool(args.dueling),
-    )
-
-    deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-4,
-        max_timesteps=args.num_timesteps,
-        buffer_size=10000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.01,
-        train_freq=4,
-        learning_starts=10000,
-        target_network_update_freq=1000,
-        gamma=0.99,
-        prioritized_replay=bool(args.prioritized),
-        prioritized_replay_alpha=args.prioritized_replay_alpha,
-        checkpoint_freq=args.checkpoint_freq,
-        checkpoint_path=args.checkpoint_path,
-    )
-
-    env.close()
-
-
-if __name__ == '__main__':
-    main()

baselines/deepq/experiments/train_cartpole.py

@@ -11,12 +11,11 @@ def callback(lcl, _glb):
 
 def main():
     env = gym.make("CartPole-v0")
-    model = deepq.models.mlp([64])
     act = deepq.learn(
         env,
-        q_func=model,
+        network='mlp',
         lr=1e-3,
-        max_timesteps=100000,
+        total_timesteps=100000,
         buffer_size=50000,
         exploration_fraction=0.1,
         exploration_final_eps=0.02,

baselines/deepq/experiments/train_mountaincar.py

@@ -1,17 +1,17 @@
 import gym
 
 from baselines import deepq
+from baselines.common import models
 
 
 def main():
     env = gym.make("MountainCar-v0")
     # Enabling layer_norm here is import for parameter space noise!
-    model = deepq.models.mlp([64], layer_norm=True)
     act = deepq.learn(
         env,
-        q_func=model,
+        network=models.mlp(num_hidden=64, num_layers=1),
         lr=1e-3,
-        max_timesteps=100000,
+        total_timesteps=100000,
         buffer_size=50000,
         exploration_fraction=0.1,
         exploration_final_eps=0.1,

baselines/deepq/experiments/train_pong.py

@@ -0,0 +1,34 @@
+from baselines import deepq
+from baselines import bench
+from baselines import logger
+from baselines.common.atari_wrappers import make_atari
+
+
+def main():
+    logger.configure()
+    env = make_atari('PongNoFrameskip-v4')
+    env = bench.Monitor(env, logger.get_dir())
+    env = deepq.wrap_atari_dqn(env)
+
+    model = deepq.learn(
+        env,
+        "conv_only",
+        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+        hiddens=[256],
+        dueling=True,
+        lr=1e-4,
+        total_timesteps=int(1e7),
+        buffer_size=10000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.01,
+        train_freq=4,
+        learning_starts=10000,
+        target_network_update_freq=1000,
+        gamma=0.99,
+    )
+
+    model.save('pong_model.pkl')
+    env.close()
+
+if __name__ == '__main__':
+    main()

baselines/deepq/models.py

@@ -89,3 +89,46 @@ def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
 
     return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs)
 
+
+
+def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
+    if isinstance(network, str):
+        from baselines.common.models import get_network_builder
+        network = get_network_builder(network)(**network_kwargs)
+
+    def q_func_builder(input_placeholder, num_actions, scope, reuse=False):
+        with tf.variable_scope(scope, reuse=reuse):
+            latent = network(input_placeholder)
+            if isinstance(latent, tuple):
+                if latent[1] is not None:
+                    raise NotImplementedError("DQN is not compatible with recurrent policies yet")
+                latent = latent[0]
+
+            latent = layers.flatten(latent)
+
+            with tf.variable_scope("action_value"):
+                action_out = latent
+                for hidden in hiddens:
+                    action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None)
+                    if layer_norm:
+                        action_out = layers.layer_norm(action_out, center=True, scale=True)
+                    action_out = tf.nn.relu(action_out)
+                action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None)
+
+            if dueling:
+                with tf.variable_scope("state_value"):
+                    state_out = latent
+                    for hidden in hiddens:
+                        state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None)
+                        if layer_norm:
+                            state_out = layers.layer_norm(state_out, center=True, scale=True)
+                        state_out = tf.nn.relu(state_out)
+                    state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None)
+                action_scores_mean = tf.reduce_mean(action_scores, 1)
+                action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
+                q_out = state_score + action_scores_centered
+            else:
+                q_out = action_scores
+            return q_out
+
+    return q_func_builder

baselines/deepq/replay_buffer.py

@@ -106,9 +106,10 @@ class PrioritizedReplayBuffer(ReplayBuffer):
 
     def _sample_proportional(self, batch_size):
         res = []
-        for _ in range(batch_size):
-            # TODO(szymon): should we ensure no repeats?
-            mass = random.random() * self._it_sum.sum(0, len(self._storage) - 1)
+        p_total = self._it_sum.sum(0, len(self._storage) - 1)
+        every_range_len = p_total / batch_size
+        for i in range(batch_size):
+            mass = random.random() * every_range_len + i * every_range_len
             idx = self._it_sum.find_prefixsum_idx(mass)
             res.append(idx)
         return res

baselines/deepq/test_identity.py

@@ -1,43 +0,0 @@
-import tensorflow as tf
-import random
-
-from baselines import deepq
-from baselines.common.identity_env import IdentityEnv
-
-
-def test_identity():
-
-    with tf.Graph().as_default():
-        env = IdentityEnv(10)
-        random.seed(0)
-
-        tf.set_random_seed(0)
-
-        param_noise = False
-        model = deepq.models.mlp([32])
-        act = deepq.learn(
-            env,
-            q_func=model,
-            lr=1e-3,
-            max_timesteps=10000,
-            buffer_size=50000,
-            exploration_fraction=0.1,
-            exploration_final_eps=0.02,
-            print_freq=10,
-            param_noise=param_noise,
-        )
-
-        tf.set_random_seed(0)
-
-        N_TRIALS = 1000
-        sum_rew = 0
-        obs = env.reset()
-        for i in range(N_TRIALS):
-            obs, rew, done, _ = env.step(act([obs]))
-            sum_rew += rew
-
-        assert sum_rew > 0.9 * N_TRIALS
-
-
-if __name__ == '__main__':
-    test_identity()