updated README files and deepq.train_cartpole example

* Several bugfixes

* Fixed ActWrapper.step bug

.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 --select=F,E999 baselines/common baselines/trpo_mpi baselines/ppo2 baselines/a2c baselines/deepq baselines/acer
+    - docker run baselines-test pytest --runslow

Dockerfile

@@ -1,20 +1,24 @@
 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 -e .[test]
+
+
 CMD /bin/bash

README.md

@@ -62,6 +62,56 @@ pip install pytest
 pytest
 ```
 
+## Subpackages
+
+## Testing the installation
+All unit tests in baselines can be run using pytest runner:
+```
+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 a2c for 20M timesteps
+```bash
+    python -m baselines.run --alg=a2c --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=a2c --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy
+```
+will set entropy coeffient 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](common/models.py) for description of network parameters for each type of model, and 
+docstring for [baselines/ppo2/ppo2.py/learn()](ppo2/ppo2.py) fir 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 5k. 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
+
+
 ## Subpackages
 
 - [A2C](baselines/a2c)
@@ -71,10 +121,19 @@ 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,
@@ -85,3 +144,4 @@ To cite this repository in publications:
       journal = {GitHub repository},
       howpublished = {\url{https://github.com/openai/baselines}},
     }
+

baselines/a2c/README.md

@@ -2,4 +2,5 @@
 
 - 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)

baselines/a2c/a2c.py

@@ -1,42 +1,48 @@
-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.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,
+    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 = policy(nenvs, 1, sess)
+            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)
+        neglogpac = train_model.pd.neglogp(A)
+        entropy = tf.reduce_mean(train_model.pd.entropy())
 
-        neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)
         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))
+        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")
+        params = find_trainable_variables("a2c_model")
         grads = tf.gradients(loss, params)
         if max_grad_norm is not None:
             grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
@@ -50,6 +56,7 @@ class Model(object):
             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 +67,6 @@ 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
@@ -78,66 +74,87 @@ 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
+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)
 
     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)
+   
+    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)
     runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
 
     nbatch = nenvs*nsteps
@@ -158,3 +175,4 @@ def learn(policy, env, seed, nsteps=5, total_timesteps=int(80e6), vf_coef=0.5, e
             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 +1,60 @@
+import numpy as np
+from baselines.a2c.utils import discount_with_dones
+from baselines.common.runners import AbstractEnvRunner
+
+class Runner(AbstractEnvRunner):
+
+    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):
+        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, S=self.states, M=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=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 +1,20 @@
 import time
-import joblib
+import functools
 import numpy as np
 import tensorflow as tf
 from baselines import logger
 
 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.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
 
 # remove last step
 def strip(var, nenvs, nsteps, flat = False):
@@ -59,10 +59,8 @@ class Model(object):
                  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
 
@@ -72,11 +70,16 @@ class Model(object):
         MU = tf.placeholder(tf.float32, [nbatch, nact]) # mu's
         LR = tf.placeholder(tf.float32, [])
         eps = 1e-6
+    
+        step_ob_placeholder = tf.placeholder(dtype=ob_space.dtype, shape=(nenvs,) + ob_space.shape[:-1] + (ob_space.shape[-1] * nstack,))
+        train_ob_placeholder = tf.placeholder(dtype=ob_space.dtype, shape=(nenvs*(nsteps+1),) + ob_space.shape[:-1] + (ob_space.shape[-1] * nstack,))
+        with tf.variable_scope('acer_model', reuse=tf.AUTO_REUSE):
 
-        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_model = policy(observ_placeholder=step_ob_placeholder, sess=sess)
+            train_model = policy(observ_placeholder=train_ob_placeholder, sess=sess)
 
-        params = find_trainable_variables("model")
+    
+        params = find_trainable_variables("acer_model")
         print("Params {}".format(len(params)))
         for var in params:
             print(var)
@@ -90,14 +93,20 @@ 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)
@@ -110,7 +119,8 @@ class Model(object):
         qret = q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma)
 
         # Calculate losses
-        # Entropy
+        # 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 +202,29 @@ 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):
@@ -311,19 +270,84 @@ class Acer():
             logger.dump_tabular()
 
 
-def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
+def learn(network, env, seed=None, nsteps=20, nstack=4, 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)
+    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
+    num_procs = len(env.remotes) if hasattr(env, 'remotes') else 1# 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,
                   max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
@@ -338,6 +362,7 @@ def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_c
     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):
@@ -346,3 +371,4 @@ def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_c
                 acer.call(on_policy=False)  # no simulation steps in this
 
     env.close()
+    return model

baselines/acer/defaults.py

@@ -0,0 +1,4 @@
+def atari():
+    return 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 +1,60 @@
+import numpy as np
+from baselines.common.runners import AbstractEnvRunner
+
+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.nact = env.action_space.n
+        nenv = self.nenv
+        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):
+        #self.obs = obs
+        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, 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.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
+

baselines/acktr/README.md

@@ -2,4 +2,7 @@
 
 - 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)
+
+

baselines/acktr/acktr.py

@@ -0,0 +1 @@
+from baselines.acktr.acktr_disc import *

baselines/acktr/acktr_disc.py

@@ -1,16 +1,17 @@
 import os.path as osp
 import time
-import joblib
+import functools
 import numpy as np
 import tensorflow as tf
 from baselines import logger
 
 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.runner import Runner
 from baselines.a2c.utils import discount_with_dones
 from baselines.a2c.utils import Scheduler, find_trainable_variables
-from baselines.a2c.utils import cat_entropy, mse
 from baselines.acktr import kfac
 
 
@@ -19,11 +20,8 @@ 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)
+
+        self.sess = sess = get_session()
         nact = ac_space.n
         nbatch = nenvs * nsteps
         A = tf.placeholder(tf.int32, [nbatch])
@@ -32,27 +30,28 @@ class Model(object):
         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)
+        neglogpac = train_model.pd.neglogp(A)
         self.logits = 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)
 
@@ -82,22 +81,10 @@ 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
@@ -105,12 +92,17 @@ 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,
+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, **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
@@ -123,6 +115,9 @@ def learn(policy, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval
         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
@@ -153,3 +148,4 @@ def learn(policy, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval
     coord.request_stop()
     coord.join(enqueue_threads)
     env.close()
+    return model

baselines/acktr/run_atari.py

@@ -6,11 +6,11 @@ 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
+from baselines.common.policies import cnn
 
 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)
+    policy_fn = cnn(env=env, one_dim_bias=True)
     learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), nprocs=num_cpu)
     env.close()
 

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',

baselines/bench/monitor.py

@@ -112,6 +112,8 @@ 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 +160,4 @@ 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

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
@@ -17,25 +21,32 @@ def make_atari_env(env_id, num_env, seed, wrapper_kwargs=None, start_index=0):
     Create a wrapped, monitored SubprocVecEnv for Atari.
     """
     if wrapper_kwargs is None: wrapper_kwargs = {}
+    mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
     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)))
+            env.seed(seed + 10000*mpi_rank + rank if seed is not None else None)
+            env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + 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)])
 
-def make_mujoco_env(env_id, seed):
+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)))
+    env = Monitor(env, os.path.join(logger.get_dir(), str(rank)), 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 +73,27 @@ 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 +103,24 @@ 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 = {}
+    for arg in args:
+        assert arg.startswith('--')
+        assert '=' in arg, 'cannot parse arg {}'.format(arg)
+        key = arg.split('=')[0][2:]
+        value = arg.split('=')[1]
+        retval[key] = value
+
+    return retval
+
+
+

baselines/common/distributions.py

@@ -85,7 +85,7 @@ 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
 
@@ -143,26 +143,26 @@ class CategoricalPd(Pd):
         # 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(
+        return tf.nn.softmax_cross_entropy_with_logits_v2(
             logits=self.logits,
             labels=one_hot_actions)
     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):

baselines/common/input.py

@@ -1,30 +1,56 @@
 import tensorflow as tf
 from gym.spaces import Discrete, Box
 
-def observation_input(ob_space, batch_size=None, name='Ob'):
-    '''
-    Build observation input with encoding depending on the 
-    observation space type
-    Params:
+def observation_placeholder(ob_space, batch_size=None, name='Ob'):
+    ''' 
+    Create placeholder to feed observations into of the size appropriate to the observation space
     
-    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
+    Parameters:
+    ----------
 
-    returns: tuple (input_placeholder, processed_input_tensor)
+    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'):
+    ''' 
+    Create placeholder to feed observations into of the size appropriate to the observation space, and add input 
+    encoder of the appropriate type. 
+    '''
+
+    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/misc_util.py

@@ -67,14 +67,21 @@ 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
     except ImportError:
         pass
     else:
-        tf.set_random_seed(i)
-    np.random.seed(i)
-    random.seed(i)
+        tf.set_random_seed(myseed)
+    np.random.seed(myseed)
+    random.seed(myseed)
 
 
 def pretty_eta(seconds_left):

baselines/common/models.py

@@ -0,0 +1,177 @@
+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
+
+
+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)))
+
+
+def mlp(num_layers=2, num_hidden=64, activation=tf.tanh):
+    """
+    Simple fully connected layer policy. Separate stacks of fully-connected layers are used for policy and value function estimation.
+    More customized fully-connected policies can be obtained by using PolicyWithV class directly.
+
+    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 placeholder
+    """        
+    def network_fn(X):
+        h = tf.layers.flatten(X)
+        for i in range(num_layers):
+            h = activation(fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2)))
+        return h, None
+
+    return network_fn
+  
+
+def cnn(**conv_kwargs):
+    def network_fn(X):
+        return nature_cnn(X, **conv_kwargs), None
+    return network_fn
+
+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, None
+    return network_fn
+
+
+
+def lstm(nlstm=128, layer_norm=False):
+    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
+
+
+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
+
+def cnn_lnlstm(nlstm=128, **conv_kwargs):
+    return cnn_lstm(nlstm, layer_norm=True, **conv_kwargs)
+
+
+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, None
+    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):
+    # TODO: replace with reflection? 
+    if name == 'cnn':
+        return cnn
+    elif name == 'cnn_small':
+        return cnn_small
+    elif name == 'conv_only':
+        return conv_only
+    elif name == 'mlp':
+        return mlp
+    elif name == 'lstm':
+        return lstm
+    elif name == 'cnn_lstm':
+        return cnn_lstm
+    elif name == 'cnn_lnlstm':
+        return cnn_lnlstm
+    else:
+        raise ValueError('Unknown network type: {}'.format(name))

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_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 +1,179 @@
+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)
+
+        self.pdtype = make_pdtype(env.action_space)
+
+        self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01)
+
+        self.action = self.pd.sample()
+        self.neglogp = self.pd.neglogp(self.action)
+        self.sess = sess
+
+        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 or tf.get_default_session()
+        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 observaion(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 observaion(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, recurrent_tensors = policy_network(encoded_x)
+
+            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):
+                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_count = batch_count + self.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 / (count + batch_count)
+    new_var = M2 / (count + batch_count)
+    new_count = batch_count + count
+    
+    return new_mean, new_var, new_count
+    
 
-        self.mean = new_mean
-        self.var = new_var
-        self.count = 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()
+
+        
 
 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/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
+import filelock
+from gym import Env
+from gym.spaces import Discrete, Box
+
+
+
+class MnistEnv(Env):
+    def __init__(
+            self,
+            seed=0,
+            episode_len=None,
+            no_images=None
+    ):
+        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 +1,40 @@
+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),
+    'acktr': dict(nsteps=32, value_network='copy'),
+    'deepq': {},
+    '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)

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 +1,55 @@
+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': {},
+    '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)
+}
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alg", learn_kwargs.keys())
+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", ['a2c', 'ppo2', 'trpo_mpi'])
+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('a2c')    
+

baselines/common/tests/test_mnist.py

@@ -0,0 +1,50 @@
+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),
+    # TODO need to resolve inference (step) API differences for acer; also slow
+    # 'acer': dict(seed=0, total_timesteps=1000),
+    '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('deepq')

baselines/common/tests/test_serialization.py

@@ -0,0 +1,97 @@
+import os
+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': {},
+    '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 ['acktr', 'trpo_mpi', 'deepq']:
+            # TODO make acktr work with recurrent policies
+            # and test
+            # github issue: https://github.com/openai/baselines/issues/194
+            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)
+
+ 
+
+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):
@@ -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 +311,95 @@ def get_available_gpus():
 # Saving variables
 # ================================================================
 
-def load_state(fname):
+def load_state(fname, sess=None):
+    sess = sess or get_session()
     saver = tf.train.Saver()
     saver.restore(tf.get_default_session(), fname)
 
-def save_state(fname):
+def save_state(fname, sess=None):
+    sess = sess or get_session()
     os.makedirs(os.path.dirname(fname), 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)}
+    os.makedirs(os.path.dirname(save_path), 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 = []
+    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):
+    from tensorboard import main as tb
+    import threading
+    tf.flags.FLAGS.logdir = log_dir
+    t = threading.Thread(target=tb.main, args=([]))
+    t.start()
 

baselines/common/vec_env/dummy_vec_env.py

@@ -30,15 +30,30 @@ 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)
-        return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
+        return (np.copy(self._obs_from_buf()), np.copy(self.buf_rews), np.copy(self.buf_dones),
                 self.buf_infos.copy())
 
     def reset(self):

baselines/common/vec_env/subproc_vec_env.py

@@ -7,26 +7,30 @@ from baselines.common.tile_images import tile_images
 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):
     def __init__(self, env_fns, spaces=None):

baselines/common/vec_env/vec_normalize.py

@@ -10,6 +10,8 @@ class VecNormalize(VecEnvWrapper):
         VecEnvWrapper.__init__(self, venv)
         self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
         self.ret_rms = RunningMeanStd(shape=()) if ret else None
+        #self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='observation_running_mean_std') if ob else None
+        #self.ret_rms = TfRunningMeanStd(shape=(), scope='return_running_mean_std') if ret else None
         self.clipob = clipob
         self.cliprew = cliprew
         self.ret = np.zeros(self.num_envs)

baselines/ddpg/ddpg.py

@@ -26,9 +26,9 @@ 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, keep_dims=True)
+    m = tf.reduce_mean(x, axis=axis, keepdims=True)
     devs_squared = tf.square(x - m)
-    return tf.reduce_mean(devs_squared, axis=axis, keep_dims=keepdims)
+    return tf.reduce_mean(devs_squared, axis=axis, keepdims=keepdims)
 
 def get_target_updates(vars, target_vars, tau):
     logger.info('setting up target updates ...')

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_apth=./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

@@ -10,20 +10,24 @@ import baselines.common.tf_util as U
 from baselines.common.tf_util import load_state, save_state
 from baselines import logger
 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
+
 
 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)
@@ -42,7 +46,10 @@ class ActWrapper(object):
     def __call__(self, *args, **kwargs):
         return self._act(*args, **kwargs)
 
-    def save(self, path=None):
+    def step(self, observation, **kwargs):
+        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")
@@ -61,8 +68,12 @@ class ActWrapper(object):
         with open(path, "wb") as f:
             cloudpickle.dump((model_data, self._act_params), f)
 
+    def save(self, path):
+        save_state(path)
+        self.save_act(path+".pickle")
 
-def load(path):
+
+def load_act(path):
     """Load act function that was returned by learn function.
 
     Parameters
@@ -76,13 +87,14 @@ def load(path):
         function that takes a batch of observations
         and returns actions.
     """
-    return ActWrapper.load(path)
+    return ActWrapper.load_act(path)
 
 
 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,
@@ -100,7 +112,10 @@ 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
@@ -119,7 +134,7 @@ def learn(env,
         and returns a tensor of shape (batch_size, num_actions) with values of every action.
     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,
@@ -194,12 +216,12 @@ def learn(env,
     }
 
     act = ActWrapper(act, act_params)
-
+  
     # Create the replay buffer
     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)
             logger.log('Loaded model from {}'.format(model_file))
             model_saved = True
+        elif load_path is not None:
+            load_state(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

baselines/deepq/defaults.py

@@ -0,0 +1,21 @@
+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
+    )
+
+def retro():
+    return atari()
+

baselines/deepq/experiments/enjoy_retro.py

@@ -0,0 +1,34 @@
+import argparse
+
+import numpy as np
+
+from baselines import deepq
+from baselines.common import retro_wrappers
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--env', help='environment ID', default='SuperMarioBros-Nes')
+    parser.add_argument('--gamestate', help='game state to load', default='Level1-1')
+    parser.add_argument('--model', help='model pickle file from ActWrapper.save', default='model.pkl')
+    args = parser.parse_args()
+
+    env = retro_wrappers.make_retro(game=args.env, state=args.gamestate, max_episode_steps=None)
+    env = retro_wrappers.wrap_deepmind_retro(env)
+    act = deepq.load(args.model)
+
+    while True:
+        obs, done = env.reset(), False
+        episode_rew = 0
+        while not done:
+            env.render()
+            action = act(obs[None])[0]
+            env_action = np.zeros(env.action_space.n)
+            env_action[action] = 1
+            obs, rew, done, _ = env.step(env_action)
+            episode_rew += rew
+        print('Episode reward', episode_rew)
+
+
+if __name__ == '__main__':
+    main()

baselines/deepq/experiments/run_retro.py

@@ -0,0 +1,49 @@
+import argparse
+
+from baselines import deepq
+from baselines.common import set_global_seeds
+from baselines import bench
+from baselines import logger
+from baselines.common import retro_wrappers
+import retro
+
+
+def main():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--env', help='environment ID', default='SuperMarioBros-Nes')
+    parser.add_argument('--gamestate', help='game state to load', default='Level1-1')
+    parser.add_argument('--seed', help='seed', type=int, default=0)
+    parser.add_argument('--num-timesteps', type=int, default=int(10e6))
+    args = parser.parse_args()
+    logger.configure()
+    set_global_seeds(args.seed)
+    env = retro_wrappers.make_retro(game=args.env, state=args.gamestate, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE)
+    env.seed(args.seed)
+    env = bench.Monitor(env, logger.get_dir())
+    env = retro_wrappers.wrap_deepmind_retro(env)
+
+    model = deepq.models.cnn_to_mlp(
+        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+        hiddens=[256],
+        dueling=True
+    )
+    act = 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=True
+    )
+    act.save()
+    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/models.py

@@ -89,3 +89,41 @@ 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)
+            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/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()

baselines/deepq/utils.py

@@ -1,4 +1,5 @@
 from baselines.common.input import observation_input
+from baselines.common.tf_util import adjust_shape
 
 import tensorflow as tf
 
@@ -36,7 +37,7 @@ class PlaceholderTfInput(TfInput):
         return self._placeholder
 
     def make_feed_dict(self, data):
-        return {self._placeholder: data}
+        return {self._placeholder: adjust_shape(self._placeholder, data)}
 
 
 class Uint8Input(PlaceholderTfInput):

baselines/ppo1/run_atari.py

@@ -18,7 +18,7 @@ def train(env_id, num_timesteps, seed):
         logger.configure()
     else:
         logger.configure(format_strs=[])
-    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
+    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() if seed is not None else None
     set_global_seeds(workerseed)
     env = make_atari(env_id)
     def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613

baselines/ppo2/README.md

@@ -2,5 +2,7 @@
 
 - Original paper: https://arxiv.org/abs/1707.06347
 - Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
-- `python -m baselines.ppo2.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
-- `python -m baselines.ppo2.run_mujoco` runs the algorithm for 1M frames on a Mujoco environment.
+
+- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment.
+- also refer to the repo-wide [README.md](../../README.md#training-models)

baselines/ppo2/defaults.py

@@ -0,0 +1,22 @@
+def mujoco():
+    return dict(
+        nsteps=2048,
+        nminibatches=32,
+        lam=0.95,
+        gamma=0.99,
+        noptepochs=10,
+        log_interval=1,
+        ent_coef=0.0,
+        lr=lambda f: 3e-4 * f,
+        cliprange=0.2,
+        value_network='copy'
+    )
+
+def atari():
+    return dict(
+        nsteps=128, nminibatches=4,
+        lam=0.95, gamma=0.99, noptepochs=4, log_interval=1,
+        ent_coef=.01,
+        lr=lambda f : f * 2.5e-4,
+        cliprange=lambda f : f * 0.1,
+    )

baselines/ppo2/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/ppo2/ppo2.py

@@ -1,21 +1,29 @@
 import os
 import time
-import joblib
+import functools
 import numpy as np
 import os.path as osp
 import tensorflow as tf
 from baselines import logger
 from collections import deque
-from baselines.common import explained_variance
+from baselines.common import explained_variance, set_global_seeds
+from baselines.common.policies import build_policy
 from baselines.common.runners import AbstractEnvRunner
+from baselines.common.tf_util import get_session, save_variables, load_variables
+from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
+
+from mpi4py import MPI
+from baselines.common.tf_util import initialize
+from baselines.common.mpi_util import sync_from_root
 
 class Model(object):
     def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
                 nsteps, ent_coef, vf_coef, max_grad_norm):
-        sess = tf.get_default_session()
+        sess = get_session()
 
-        act_model = policy(sess, ob_space, ac_space, nbatch_act, 1, reuse=False)
-        train_model = policy(sess, ob_space, ac_space, nbatch_train, nsteps, reuse=True)
+        with tf.variable_scope('ppo2_model', reuse=tf.AUTO_REUSE):
+            act_model = policy(nbatch_act, 1, sess)
+            train_model = policy(nbatch_train, nsteps, sess)
 
         A = train_model.pdtype.sample_placeholder([None])
         ADV = tf.placeholder(tf.float32, [None])
@@ -40,14 +48,16 @@ class Model(object):
         approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))
         clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
         loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
-        with tf.variable_scope('model'):
-            params = tf.trainable_variables()
-        grads = tf.gradients(loss, params)
+        params = tf.trainable_variables('ppo2_model')
+        trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+        grads_and_var = trainer.compute_gradients(loss, params)
+        grads, var = zip(*grads_and_var)
+
         if max_grad_norm is not None:
             grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
-        grads = list(zip(grads, params))
-        trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
-        _train = trainer.apply_gradients(grads)
+        grads_and_var = list(zip(grads, var))
+
+        _train = trainer.apply_gradients(grads_and_var)
 
         def train(lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):
             advs = returns - values
@@ -63,17 +73,6 @@ class Model(object):
             )[:-1]
         self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']
 
-        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)
-            # If you want to load weights, also save/load observation scaling inside VecNormalize
 
         self.train = train
         self.train_model = train_model
@@ -81,9 +80,14 @@ class Model(object):
         self.step = act_model.step
         self.value = act_model.value
         self.initial_state = act_model.initial_state
-        self.save = save
-        self.load = load
-        tf.global_variables_initializer().run(session=sess) #pylint: disable=E1101
+
+        self.save = functools.partial(save_variables, sess=sess)
+        self.load = functools.partial(load_variables, sess=sess)
+
+        if MPI.COMM_WORLD.Get_rank() == 0:
+            initialize()
+        global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
+        sync_from_root(sess, global_variables) #pylint: disable=E1101
 
 class Runner(AbstractEnvRunner):
 
@@ -97,7 +101,7 @@ class Runner(AbstractEnvRunner):
         mb_states = self.states
         epinfos = []
         for _ in range(self.nsteps):
-            actions, values, self.states, neglogpacs = self.model.step(self.obs, self.states, self.dones)
+            actions, values, self.states, neglogpacs = self.model.step(self.obs, S=self.states, M=self.dones)
             mb_obs.append(self.obs.copy())
             mb_actions.append(actions)
             mb_values.append(values)
@@ -115,7 +119,7 @@ class Runner(AbstractEnvRunner):
         mb_values = np.asarray(mb_values, dtype=np.float32)
         mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)
         mb_dones = np.asarray(mb_dones, dtype=np.bool)
-        last_values = self.model.value(self.obs, self.states, self.dones)
+        last_values = self.model.value(self.obs, S=self.states, M=self.dones)
         #discount/bootstrap off value fn
         mb_returns = np.zeros_like(mb_rewards)
         mb_advs = np.zeros_like(mb_rewards)
@@ -145,10 +149,65 @@ def constfn(val):
         return val
     return f
 
-def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
+def learn(*, network, env, total_timesteps, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
             vf_coef=0.5,  max_grad_norm=0.5, gamma=0.99, lam=0.95,
             log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
-            save_interval=0, load_path=None):
+            save_interval=0, load_path=None, **network_kwargs):
+    '''
+    Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
+    
+    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: baselines.common.vec_env.VecEnv     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)
+
+    total_timesteps: int              number of timesteps (i.e. number of actions taken in the environment)
+
+    ent_coef: float                   policy entropy coefficient in the optimization objective
+
+    lr: float or function             learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the 
+                                      training and 0 is the end of the training.
+
+    vf_coef: float                    value function loss coefficient in the optimization objective
+
+    max_grad_norm: float or None      gradient norm clipping coefficient
+    
+    gamma: float                      discounting factor
+
+    lam: float                        advantage estimation discounting factor (lambda in the paper)
+
+    log_interval: int                 number of timesteps between logging events
+
+    nminibatches: int                 number of training minibatches per update
+
+    noptepochs: int                   number of training epochs per update
+
+    cliprange: float or function      clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training 
+                                      and 0 is the end of the training 
+
+    save_interval: int                number of timesteps between saving events
+
+    load_path: str                    path to load the model from
+
+    **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. 
+
+    
+
+    '''
+    
+    set_global_seeds(seed)
 
     if isinstance(lr, float): lr = constfn(lr)
     else: assert callable(lr)
@@ -156,6 +215,8 @@ def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
     else: assert callable(cliprange)
     total_timesteps = int(total_timesteps)
 
+    policy = build_policy(env, network, **network_kwargs)
+
     nenvs = env.num_envs
     ob_space = env.observation_space
     ac_space = env.action_space
@@ -180,7 +241,6 @@ def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
     nupdates = total_timesteps//nbatch
     for update in range(1, nupdates+1):
         assert nbatch % nminibatches == 0
-        nbatch_train = nbatch // nminibatches
         tstart = time.time()
         frac = 1.0 - (update - 1.0) / nupdates
         lrnow = lr(frac)
@@ -228,8 +288,9 @@ def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
             logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
                 logger.logkv(lossname, lossval)
-            logger.dumpkvs()
-        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():
+            if MPI.COMM_WORLD.Get_rank() == 0:
+                logger.dumpkvs()
+        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and MPI.COMM_WORLD.Get_rank() == 0:
             checkdir = osp.join(logger.get_dir(), 'checkpoints')
             os.makedirs(checkdir, exist_ok=True)
             savepath = osp.join(checkdir, '%.5i'%update)
@@ -240,3 +301,6 @@ def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
 
 def safemean(xs):
     return np.nan if len(xs) == 0 else np.mean(xs)
+
+
+

baselines/ppo2/run_atari.py

@@ -1,40 +0,0 @@
-#!/usr/bin/env python3
-import sys
-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.ppo2 import ppo2
-from baselines.ppo2.policies import CnnPolicy, LstmPolicy, LnLstmPolicy, MlpPolicy
-import multiprocessing
-import tensorflow as tf
-
-
-def train(env_id, num_timesteps, seed, policy):
-
-    ncpu = multiprocessing.cpu_count()
-    if sys.platform == 'darwin': ncpu //= 2
-    config = tf.ConfigProto(allow_soft_placement=True,
-                            intra_op_parallelism_threads=ncpu,
-                            inter_op_parallelism_threads=ncpu)
-    config.gpu_options.allow_growth = True #pylint: disable=E1101
-    tf.Session(config=config).__enter__()
-
-    env = VecFrameStack(make_atari_env(env_id, 8, seed), 4)
-    policy = {'cnn' : CnnPolicy, 'lstm' : LstmPolicy, 'lnlstm' : LnLstmPolicy, 'mlp': MlpPolicy}[policy]
-    ppo2.learn(policy=policy, env=env, nsteps=128, nminibatches=4,
-        lam=0.95, gamma=0.99, noptepochs=4, log_interval=1,
-        ent_coef=.01,
-        lr=lambda f : f * 2.5e-4,
-        cliprange=lambda f : f * 0.1,
-        total_timesteps=int(num_timesteps * 1.1))
-
-def main():
-    parser = atari_arg_parser()
-    parser.add_argument('--policy', help='Policy architecture', choices=['cnn', 'lstm', 'lnlstm', 'mlp'], default='cnn')
-    args = parser.parse_args()
-    logger.configure()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
-        policy=args.policy)
-
-if __name__ == '__main__':
-    main()

baselines/ppo2/run_mujoco.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-import numpy as np
-from baselines.common.cmd_util import mujoco_arg_parser
-from baselines import bench, logger
-
-
-def train(env_id, num_timesteps, seed):
-    from baselines.common import set_global_seeds
-    from baselines.common.vec_env.vec_normalize import VecNormalize
-    from baselines.ppo2 import ppo2
-    from baselines.ppo2.policies import MlpPolicy
-    import gym
-    import tensorflow as tf
-    from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-    ncpu = 1
-    config = tf.ConfigProto(allow_soft_placement=True,
-                            intra_op_parallelism_threads=ncpu,
-                            inter_op_parallelism_threads=ncpu)
-    tf.Session(config=config).__enter__()
-
-    def make_env():
-        env = gym.make(env_id)
-        env = bench.Monitor(env, logger.get_dir(), allow_early_resets=True)
-        return env
-
-    env = DummyVecEnv([make_env])
-    env = VecNormalize(env)
-
-    set_global_seeds(seed)
-    policy = MlpPolicy
-    model = ppo2.learn(policy=policy, env=env, nsteps=2048, nminibatches=32,
-                       lam=0.95, gamma=0.99, noptepochs=10, log_interval=1,
-                       ent_coef=0.0,
-                       lr=3e-4,
-                       cliprange=0.2,
-                       total_timesteps=num_timesteps)
-
-    return model, env
-
-
-def main():
-    args = mujoco_arg_parser().parse_args()
-    logger.configure()
-    model, env = train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
-
-    if args.play:
-        logger.log("Running trained model")
-        obs = np.zeros((env.num_envs,) + env.observation_space.shape)
-        obs[:] = env.reset()
-        while True:
-            actions = model.step(obs)[0]
-            obs[:]  = env.step(actions)[0]
-            env.render()
-
-
-if __name__ == '__main__':
-    main()

baselines/run.py

@@ -0,0 +1,234 @@
+import sys
+import multiprocessing 
+import os
+import os.path as osp
+import gym
+from collections import defaultdict
+import tensorflow as tf
+
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
+from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_mujoco_env, make_atari_env
+from baselines.common.tf_util import save_state, load_state, get_session
+from baselines import bench, logger
+from importlib import import_module
+
+from baselines.common.vec_env.vec_normalize import VecNormalize
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
+from baselines.common import atari_wrappers, retro_wrappers
+
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
+_game_envs = defaultdict(set)
+for env in gym.envs.registry.all():
+    # solve this with regexes
+    env_type = env._entry_point.split(':')[0].split('.')[-1]
+    _game_envs[env_type].add(env.id)
+
+# reading benchmark names directly from retro requires 
+# importing retro here, and for some reason that crashes tensorflow 
+# in ubuntu 
+_game_envs['retro'] = set([
+    'BubbleBobble-Nes',
+    'SuperMarioBros-Nes',
+    'TwinBee3PokoPokoDaimaou-Nes',
+    'SpaceHarrier-Nes',
+    'SonicTheHedgehog-Genesis',
+    'Vectorman-Genesis',
+    'FinalFight-Snes',
+    'SpaceInvaders-Snes',
+])
+
+
+def train(args, extra_args):
+    env_type, env_id = get_env_type(args.env)
+        
+    total_timesteps = int(args.num_timesteps)
+    seed = args.seed
+
+    learn = get_learn_function(args.alg)
+    alg_kwargs = get_learn_function_defaults(args.alg, env_type)
+    alg_kwargs.update(extra_args)
+
+    env = build_env(args)
+
+    if args.network:
+        alg_kwargs['network'] = args.network
+    else:
+        if alg_kwargs.get('network') is None:
+            alg_kwargs['network'] = get_default_network(env_type)
+ 
+       
+    
+    print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs))
+
+    model = learn(
+        env=env,  
+        seed=seed,
+        total_timesteps=total_timesteps,
+        **alg_kwargs
+    )
+
+    return model, env
+
+
+def build_env(args, render=False):
+    ncpu = multiprocessing.cpu_count()
+    if sys.platform == 'darwin': ncpu //= 2
+    nenv = args.num_env or ncpu if not render else 1
+    alg = args.alg
+    rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+    seed = args.seed    
+
+    env_type, env_id = get_env_type(args.env)
+    if env_type == 'mujoco':
+        get_session(tf.ConfigProto(allow_soft_placement=True,
+                                   intra_op_parallelism_threads=1, 
+                                   inter_op_parallelism_threads=1))
+
+        if args.num_env:
+            env = SubprocVecEnv([lambda: make_mujoco_env(env_id, seed + i if seed is not None else None, args.reward_scale) for i in range(args.num_env)])    
+        else:
+            env = DummyVecEnv([lambda: make_mujoco_env(env_id, seed, args.reward_scale)])
+
+        env = VecNormalize(env)
+
+    elif env_type == 'atari':
+        if alg == 'acer':
+            env = make_atari_env(env_id, nenv, seed)
+        elif alg == 'deepq':
+            env = atari_wrappers.make_atari(env_id)
+            env.seed(seed)
+            env = bench.Monitor(env, logger.get_dir())
+            env = atari_wrappers.wrap_deepmind(env, frame_stack=True, scale=True)
+        elif alg == 'trpo_mpi':
+            env = atari_wrappers.make_atari(env_id)
+            env.seed(seed)
+            env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
+            env = atari_wrappers.wrap_deepmind(env)
+            # TODO check if the second seeding is necessary, and eventually remove
+            env.seed(seed)
+        else:
+            frame_stack_size = 4
+            env = VecFrameStack(make_atari_env(env_id, nenv, seed), frame_stack_size)
+
+    elif env_type == 'retro':
+        import retro
+        gamestate = args.gamestate or 'Level1-1'
+        env = retro_wrappers.make_retro(game=args.env, state=gamestate, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE)
+        env.seed(args.seed)
+        env = bench.Monitor(env, logger.get_dir())
+        env = retro_wrappers.wrap_deepmind_retro(env)
+        
+    elif env_type == 'classic_control':
+        def make_env():
+            e = gym.make(env_id)
+            e = bench.Monitor(e, logger.get_dir(), allow_early_resets=True)
+            e.seed(seed)
+            return e
+            
+        env = DummyVecEnv([make_env])
+
+    else:
+        raise ValueError('Unknown env_type {}'.format(env_type))
+
+    return env
+
+
+def get_env_type(env_id):
+    if env_id in _game_envs.keys():
+        env_type = env_id
+        env_id =  [g for g in _game_envs[env_type]][0]
+    else:
+        env_type = None
+        for g, e in _game_envs.items():
+            if env_id in e:
+                env_type = g
+                break 
+        assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys())
+
+    return env_type, env_id
+
+def get_default_network(env_type):
+    if env_type == 'mujoco' or env_type == 'classic_control':
+        return 'mlp'
+    if env_type == 'atari':
+        return 'cnn'
+
+    raise ValueError('Unknown env_type {}'.format(env_type))
+    
+def get_alg_module(alg, submodule=None):
+    submodule = submodule or alg
+    try:
+        # first try to import the alg module from baselines
+        alg_module = import_module('.'.join(['baselines', alg, submodule]))
+    except ImportError:
+        # then from rl_algs
+        alg_module = import_module('.'.join(['rl_' + 'algs', alg, submodule]))
+    
+    return alg_module
+        
+
+def get_learn_function(alg):
+    return get_alg_module(alg).learn
+
+def get_learn_function_defaults(alg, env_type):
+    try:
+        alg_defaults = get_alg_module(alg, 'defaults')
+        kwargs = getattr(alg_defaults, env_type)()
+    except (ImportError, AttributeError):
+        kwargs = {}       
+    return kwargs
+    
+def parse(v): 
+    '''
+    convert value of a command-line arg to a python object if possible, othewise, keep as string
+    '''
+
+    assert isinstance(v, str)
+    try:
+        return eval(v) 
+    except (NameError, SyntaxError): 
+        return v
+
+
+def main():
+    # configure logger, disable logging in child MPI processes (with rank > 0) 
+            
+    arg_parser = common_arg_parser()
+    args, unknown_args = arg_parser.parse_known_args()
+    extra_args = {k: parse(v) for k,v in parse_unknown_args(unknown_args).items()}
+
+    
+    if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
+        rank = 0
+        logger.configure()
+    else:
+        logger.configure(format_strs = [])
+        rank = MPI.COMM_WORLD.Get_rank()
+
+    model, _ = train(args, extra_args)
+
+    if args.save_path is not None and rank == 0:
+        save_path = osp.expanduser(args.save_path)
+        model.save(save_path)
+    
+
+    if args.play:
+        logger.log("Running trained model")
+        env = build_env(args, render=True)
+        obs = env.reset()
+        while True:
+            actions = model.step(obs)[0]
+            obs, _, done, _  = env.step(actions)
+            env.render()
+            if done:
+                obs = env.reset()
+            
+
+
+if __name__ == '__main__':
+    main()

baselines/trpo_mpi/README.md

@@ -2,5 +2,6 @@
 
 - Original paper: https://arxiv.org/abs/1502.05477
 - Baselines blog post https://blog.openai.com/openai-baselines-ppo/
-- `mpirun -np 16 python -m baselines.trpo_mpi.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
-- `python -m baselines.trpo_mpi.run_mujoco` runs the algorithm for 1M timesteps on a Mujoco environment.
+- `mpirun -np 16 python -m baselines.run --alg=trpo_mpi --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
+- `python -m baselines.run --alg=trpo_mpi --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M timesteps on a Mujoco Ant environment. 
+- also refer to the repo-wide [README.md](../../README.md#training-models)

baselines/trpo_mpi/defaults.py

@@ -0,0 +1,30 @@
+from rl_common.models import mlp, cnn_small
+
+
+def atari():
+    return dict(
+        network = cnn_small(),
+        timesteps_per_batch=512, 
+        max_kl=0.001,
+        cg_iters=10,
+        cg_damping=1e-3,
+        gamma=0.98,
+        lam=1.0,
+        vf_iters=3,
+        vf_stepsize=1e-4,
+        entcoeff=0.00,
+    )
+
+def mujoco():
+    return dict(
+        network = mlp(num_hidden=32, num_layers=2),
+        timesteps_per_batch=1024,
+        max_kl=0.01,
+        cg_iters=10,
+        cg_damping=0.1,
+        gamma=0.99,
+        lam=0.98,
+        vf_iters=5,
+        vf_stepsize=1e-3,
+        normalize_observations=True, 
+    )

baselines/trpo_mpi/nosharing_cnn_policy.py

@@ -1,56 +0,0 @@
-import baselines.common.tf_util as U
-import tensorflow as tf
-import gym
-from baselines.common.distributions import make_pdtype
-
-class CnnPolicy(object):
-    recurrent = False
-    def __init__(self, name, ob_space, ac_space):
-        with tf.variable_scope(name):
-            self._init(ob_space, ac_space)
-            self.scope = tf.get_variable_scope().name
-
-    def _init(self, ob_space, ac_space):
-        assert isinstance(ob_space, gym.spaces.Box)
-
-        self.pdtype = pdtype = make_pdtype(ac_space)
-        sequence_length = None
-
-        ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
-
-        obscaled = ob / 255.0
-
-        with tf.variable_scope("pol"):
-            x = obscaled
-            x = tf.nn.relu(U.conv2d(x, 8, "l1", [8, 8], [4, 4], pad="VALID"))
-            x = tf.nn.relu(U.conv2d(x, 16, "l2", [4, 4], [2, 2], pad="VALID"))
-            x = U.flattenallbut0(x)
-            x = tf.nn.relu(tf.layers.dense(x, 128, name='lin', kernel_initializer=U.normc_initializer(1.0)))
-            logits = tf.layers.dense(x, pdtype.param_shape()[0], name='logits', kernel_initializer=U.normc_initializer(0.01))
-            self.pd = pdtype.pdfromflat(logits)
-        with tf.variable_scope("vf"):
-            x = obscaled
-            x = tf.nn.relu(U.conv2d(x, 8, "l1", [8, 8], [4, 4], pad="VALID"))
-            x = tf.nn.relu(U.conv2d(x, 16, "l2", [4, 4], [2, 2], pad="VALID"))
-            x = U.flattenallbut0(x)
-            x = tf.nn.relu(tf.layers.dense(x, 128, name='lin', kernel_initializer=U.normc_initializer(1.0)))
-            self.vpred = tf.layers.dense(x, 1, name='value', kernel_initializer=U.normc_initializer(1.0))
-            self.vpredz = self.vpred
-
-        self.state_in = []
-        self.state_out = []
-
-        stochastic = tf.placeholder(dtype=tf.bool, shape=())
-        ac = self.pd.sample()
-        self._act = U.function([stochastic, ob], [ac, self.vpred])
-
-    def act(self, stochastic, ob):
-        ac1, vpred1 =  self._act(stochastic, ob[None])
-        return ac1[0], vpred1[0]
-    def get_variables(self):
-        return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope)
-    def get_trainable_variables(self):
-        return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)
-    def get_initial_state(self):
-        return []
-

baselines/trpo_mpi/run_atari.py

@@ -1,43 +0,0 @@
-    #!/usr/bin/env python3
-from mpi4py import MPI
-from baselines.common import set_global_seeds
-import os.path as osp
-import gym, logging
-from baselines import logger
-from baselines import bench
-from baselines.common.atari_wrappers import make_atari, wrap_deepmind
-from baselines.common.cmd_util import atari_arg_parser
-
-def train(env_id, num_timesteps, seed):
-    from baselines.trpo_mpi.nosharing_cnn_policy import CnnPolicy
-    from baselines.trpo_mpi import trpo_mpi
-    import baselines.common.tf_util as U
-    rank = MPI.COMM_WORLD.Get_rank()
-    sess = U.single_threaded_session()
-    sess.__enter__()
-    if rank == 0:
-        logger.configure()
-    else:
-        logger.configure(format_strs=[])
-
-    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
-    set_global_seeds(workerseed)
-    env = make_atari(env_id)
-    def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
-        return CnnPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space)
-    env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
-    env.seed(workerseed)
-
-    env = wrap_deepmind(env)
-    env.seed(workerseed)
-
-    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3,
-        max_timesteps=int(num_timesteps * 1.1), gamma=0.98, lam=1.0, vf_iters=3, vf_stepsize=1e-4, entcoeff=0.00)
-    env.close()
-
-def main():
-    args = atari_arg_parser().parse_args()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
-
-if __name__ == "__main__":
-    main()

baselines/trpo_mpi/run_mujoco.py

@@ -1,36 +0,0 @@
-#!/usr/bin/env python3
-# noinspection PyUnresolvedReferences
-from mpi4py import MPI
-from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
-from baselines import logger
-from baselines.ppo1.mlp_policy import MlpPolicy
-from baselines.trpo_mpi import trpo_mpi
-
-def train(env_id, num_timesteps, seed):
-    import baselines.common.tf_util as U
-    sess = U.single_threaded_session()
-    sess.__enter__()
-
-    rank = MPI.COMM_WORLD.Get_rank()
-    if rank == 0:
-        logger.configure()
-    else:
-        logger.configure(format_strs=[])
-        logger.set_level(logger.DISABLED)
-    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
-    def policy_fn(name, ob_space, ac_space):
-        return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
-            hid_size=32, num_hid_layers=2)
-    env = make_mujoco_env(env_id, workerseed)
-    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
-        max_timesteps=num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
-    env.close()
-
-def main():
-    args = mujoco_arg_parser().parse_args()
-    train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
-
-
-if __name__ == '__main__':
-    main()
-

baselines/trpo_mpi/trpo_mpi.py

@@ -6,8 +6,11 @@ import time
 from baselines.common import colorize
 from mpi4py import MPI
 from collections import deque
+from baselines.common import set_global_seeds
 from baselines.common.mpi_adam import MpiAdam
 from baselines.common.cg import cg
+from baselines.common.input import observation_placeholder
+from baselines.common.policies import build_policy
 from contextlib import contextmanager
 
 def traj_segment_generator(pi, env, horizon, stochastic):
@@ -33,7 +36,7 @@ def traj_segment_generator(pi, env, horizon, stochastic):
 
     while True:
         prevac = ac
-        ac, vpred = pi.act(stochastic, ob)
+        ac, vpred, _, _ = pi.step(ob, stochastic=stochastic)
         # Slight weirdness here because we need value function at time T
         # before returning segment [0, T-1] so we get the correct
         # terminal value
@@ -41,7 +44,7 @@ def traj_segment_generator(pi, env, horizon, stochastic):
             yield {"ob" : obs, "rew" : rews, "vpred" : vpreds, "new" : news,
                     "ac" : acs, "prevac" : prevacs, "nextvpred": vpred * (1 - new),
                     "ep_rets" : ep_rets, "ep_lens" : ep_lens}
-            _, vpred = pi.act(stochastic, ob)
+            _, vpred, _, _ = pi.step(ob, stochastic=stochastic)
             # Be careful!!! if you change the downstream algorithm to aggregate
             # several of these batches, then be sure to do a deepcopy
             ep_rets = []
@@ -79,30 +82,100 @@ def add_vtarg_and_adv(seg, gamma, lam):
         gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
     seg["tdlamret"] = seg["adv"] + seg["vpred"]
 
-def learn(env, policy_fn, *,
-        timesteps_per_batch, # what to train on
-        max_kl, cg_iters,
-        gamma, lam, # advantage estimation
+def learn(*,
+        network, 
+        env,
+        total_timesteps, 
+        timesteps_per_batch=1024, # what to train on
+        max_kl=0.001, 
+        cg_iters=10,   
+        gamma=0.99, 
+        lam=1.0, # advantage estimation
+        seed=None,
         entcoeff=0.0,
         cg_damping=1e-2,
         vf_stepsize=3e-4,
         vf_iters =3,
-        max_timesteps=0, max_episodes=0, max_iters=0,  # time constraint
-        callback=None
+        max_episodes=0, max_iters=0,  # time constraint
+        callback=None,
+        load_path=None,
+        **network_kwargs
         ):
+    '''
+    learn a policy function with TRPO algorithm
+    
+    Parameters:
+    ----------
+
+    network                 neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
+                            or function that takes input placeholder and returns tuple (output, None) for feedforward nets
+                            or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
+
+    env                     environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
+
+    timesteps_per_batch     timesteps per gradient estimation batch
+
+    max_kl                  max KL divergence between old policy and new policy ( KL(pi_old || pi) )
+
+    entcoeff                coefficient of policy entropy term in the optimization objective
+
+    cg_iters                number of iterations of conjugate gradient algorithm
+
+    cg_damping              conjugate gradient damping 
+
+    vf_stepsize             learning rate for adam optimizer used to optimie value function loss
+
+    vf_iters                number of iterations of value function optimization iterations per each policy optimization step
+
+    total_timesteps           max number of timesteps
+
+    max_episodes            max number of episodes
+    
+    max_iters               maximum number of policy optimization iterations
+
+    callback                function to be called with (locals(), globals()) each policy optimization step
+    
+    load_path               str, path to load the model from (default: None, i.e. no model is loaded)
+
+    **network_kwargs        keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
+
+    Returns:
+    -------
+
+    learnt model
+
+    '''
+    
+    
     nworkers = MPI.COMM_WORLD.Get_size()
     rank = MPI.COMM_WORLD.Get_rank()
+
+    cpus_per_worker = 1
+    U.get_session(config=tf.ConfigProto(
+            allow_soft_placement=True, 
+            inter_op_parallelism_threads=cpus_per_worker,
+            intra_op_parallelism_threads=cpus_per_worker
+    ))
+    
+
+    policy = build_policy(env, network, value_network='copy', **network_kwargs)
+    set_global_seeds(seed)
+
     np.set_printoptions(precision=3)
     # Setup losses and stuff
     # ----------------------------------------
     ob_space = env.observation_space
     ac_space = env.action_space
-    pi = policy_fn("pi", ob_space, ac_space)
-    oldpi = policy_fn("oldpi", ob_space, ac_space)
+
+    ob = observation_placeholder(ob_space)
+    with tf.variable_scope("pi"):
+        pi = policy(observ_placeholder=ob)
+    with tf.variable_scope("oldpi"):
+        oldpi = policy(observ_placeholder=ob)
+
     atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
     ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
 
-    ob = U.get_placeholder_cached(name="ob")
     ac = pi.pdtype.sample_placeholder([None])
 
     kloldnew = oldpi.pd.kl(pi.pd)
@@ -111,7 +184,7 @@ def learn(env, policy_fn, *,
     meanent = tf.reduce_mean(ent)
     entbonus = entcoeff * meanent
 
-    vferr = tf.reduce_mean(tf.square(pi.vpred - ret))
+    vferr = tf.reduce_mean(tf.square(pi.vf - ret))
 
     ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold
     surrgain = tf.reduce_mean(ratio * atarg)
@@ -122,9 +195,12 @@ def learn(env, policy_fn, *,
 
     dist = meankl
 
-    all_var_list = pi.get_trainable_variables()
-    var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
-    vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
+    all_var_list = get_trainable_variables("pi")
+    # var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
+    # vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
+    var_list = get_pi_trainable_variables("pi")
+    vf_var_list = get_vf_trainable_variables("pi")
+
     vfadam = MpiAdam(vf_var_list)
 
     get_flat = U.GetFlat(var_list)
@@ -142,7 +218,8 @@ def learn(env, policy_fn, *,
     fvp = U.flatgrad(gvp, var_list)
 
     assign_old_eq_new = U.function([],[], updates=[tf.assign(oldv, newv)
-        for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())])
+        for (oldv, newv) in zipsame(get_variables("oldpi"), get_variables("pi"))])
+
     compute_losses = U.function([ob, ac, atarg], losses)
     compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)])
     compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
@@ -166,6 +243,9 @@ def learn(env, policy_fn, *,
         return out
 
     U.initialize()
+    if load_path is not None:
+        pi.load(load_path)
+    
     th_init = get_flat()
     MPI.COMM_WORLD.Bcast(th_init, root=0)
     set_from_flat(th_init)
@@ -183,11 +263,16 @@ def learn(env, policy_fn, *,
     lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
     rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
 
-    assert sum([max_iters>0, max_timesteps>0, max_episodes>0])==1
+    if sum([max_iters>0, total_timesteps>0, max_episodes>0])==0:
+        # noththing to be done
+        return pi
+
+    assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
+        'out of max_iters, total_timesteps, and max_episodes only one should be specified'
 
     while True:
         if callback: callback(locals(), globals())
-        if max_timesteps and timesteps_so_far >= max_timesteps:
+        if total_timesteps and timesteps_so_far >= total_timesteps:
             break
         elif max_episodes and episodes_so_far >= max_episodes:
             break
@@ -287,5 +372,20 @@ def learn(env, policy_fn, *,
         if rank==0:
             logger.dump_tabular()
 
+    return pi
+
 def flatten_lists(listoflists):
-    return [el for list_ in listoflists for el in list_]
+    return [el for list_ in listoflists for el in list_]
+
+def get_variables(scope):
+    return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope)
+
+def get_trainable_variables(scope):
+    return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
+
+def get_vf_trainable_variables(scope):
+    return [v for v in get_trainable_variables(scope) if 'vf' in v.name[len(scope):].split('/')]    
+
+def get_pi_trainable_variables(scope):
+    return [v for v in get_trainable_variables(scope) if 'pi' in v.name[len(scope):].split('/')]    
+

conftest.py

@@ -0,0 +1,19 @@
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption('--runslow', action='store_true', default=False, help='run slow tests')
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption('--runslow'):
+        # --runslow given in cli: do not skip slow tests
+        return
+    skip_slow = pytest.mark.skip(reason='need --runslow option to run')
+    slow_tests = []
+    for item in items:
+        if 'slow' in item.keywords:
+            slow_tests.append(item.name)
+            item.add_marker(skip_slow)
+
+    print('skipping slow tests', ' '.join(slow_tests), 'use --runslow to run this')

setup.py

@@ -14,7 +14,6 @@ setup(name='baselines',
           'scipy',
           'tqdm',
           'joblib',
-          'zmq',
           'dill',
           'progressbar2',
           'mpi4py',
@@ -23,6 +22,12 @@ setup(name='baselines',
           'click',
           'opencv-python'
       ],
+      extras_require={
+        'test': [
+            'filelock',
+            'pytest'
+        ]
+      },
       description='OpenAI baselines: high quality implementations of reinforcement learning algorithms',
       author='OpenAI',
       url='https://github.com/openai/baselines',