RNN support for PPO2 (#859)

* initial implementaion of ppo2_rnn.

* set lstm memory as tf.GraphKeys.LOCAL_VARIABLES.

* replace dones with tf.placeholder_with_default.

* improves for 'play' option.

* removed unnecessary TODO .

* improve lstm code.

* move learning rate placeholer to optimizer scope.

* support the microbatched model.

* sync cnn lstm layer with originals.

* add cnn_lnlstm layer.

* fix a case when `states` is None.

* add initial_state variable to help test.

* make ppo2 rnn test available.

* rename 'obs' with 'observations'.
rename 'transition' with 'transitions'.
fix forgetting `dones` in the replay buffer.
fix a misuse of `states` and `next_states` in the replay buffer.

* make initialization once.
make `test_fixed_sequence` compatible with ppo2.

* adjust input shape.

* fix checking of a model input args in `simple_test` function.

* disable warning on purpose.

* support the play.

* improve scopes to compatible with multiple models (i.e, other tensorflow global/local variables)

* clean the scope of ppo2 policy model.

* name the memory variable of PPO RNNs more describly

* wrap the initializations in ppo2.

* remove redundant lines.

* update `REAMD.md`.

* add RNN layers.

* add the result of HalfCheeta-v2 env  experiment.

* correct a typo.

* add RNN class.

* rename `nlstm` with `num_units` in RNN builder functions.

* remove state saving.

* reuse RNNs in a2c.utils.

* revert baselines/run.py.

* replace `ppo2.step()` with original interface.

* revert `baselines/common/tests/util.py`.

* remove redundant lines.

* revert `baselines/common/test/util.py` to b875fb7.

* remove `states` variable.

* move RNN class to `baselines/ppo2/layers.py' and revert `baselines/common/models.py` to 858afa8.

* rename `model.step_as_dict` with `model.step_with_dict`.

* removed `ppo_lstm_mlp`.

* fix 02e26fd.

.travis.yml

@@ -11,4 +11,4 @@ install:
 
 script:
     - flake8 . --show-source --statistics
-    - docker run -e RUNSLOW=1 baselines-test pytest -v .
+    - docker run baselines-test pytest -v .

README.md

@@ -109,7 +109,7 @@ This should get to the mean reward per episode about 20. To load and visualize t
 python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
 ```
 
-*NOTE:* Mujoco environments require normalization to work properly, so we wrap them with VecNormalize wrapper. Currently, to ensure the models are saved with normalization (so that trained models can be restored and run without further training) the normalization coefficients are saved as tensorflow variables. This can decrease the performance somewhat, so if you require high-throughput steps with Mujoco and do not need saving/restoring the models, it may make sense to use numpy normalization instead. To do that, set 'use_tf=False` in [baselines/run.py](baselines/run.py#L116). 
+*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
 
 ## Loading and vizualizing learning curves and other training metrics
 See [here](docs/viz/viz.ipynb) for instructions on how to load and display the training data. 

baselines/acktr/acktr.py

@@ -92,7 +92,7 @@ class Model(object):
         self.initial_state = step_model.initial_state
         tf.global_variables_initializer().run(session=sess)
 
-def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=100, 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', load_path=None, is_async=True, **network_kwargs):
     set_global_seeds(seed)

baselines/acktr/kfac.py

@@ -11,7 +11,7 @@ KFAC_DEBUG = False
 
 
 class KfacOptimizer():
-    # note that KfacOptimizer will be truly synchronous (and thus deterministic) only if a single-threaded session is used
+
     def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60, full_stats_init=False, cold_iter=100, cold_lr=None, is_async=False, async_stats=False, epsilon=1e-2, stats_decay=0.95, blockdiag_bias=False, channel_fac=False, factored_damping=False, approxT2=False, use_float64=False, weight_decay_dict={},max_grad_norm=0.5):
         self.max_grad_norm = max_grad_norm
         self._lr = learning_rate

baselines/common/atari_wrappers.py

@@ -130,60 +130,27 @@ class ClipRewardEnv(gym.RewardWrapper):
         """Bin reward to {+1, 0, -1} by its sign."""
         return np.sign(reward)
 
-
 class WarpFrame(gym.ObservationWrapper):
-    def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
-        """
-        Warp frames to 84x84 as done in the Nature paper and later work.
-
-        If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
-        observation should be warped.
-        """
-        super().__init__(env)
-        self._width = width
-        self._height = height
-        self._grayscale = grayscale
-        self._key = dict_space_key
-        if self._grayscale:
-            num_colors = 1
+    def __init__(self, env, width=84, height=84, grayscale=True):
+        """Warp frames to 84x84 as done in the Nature paper and later work."""
+        gym.ObservationWrapper.__init__(self, env)
+        self.width = width
+        self.height = height
+        self.grayscale = grayscale
+        if self.grayscale:
+            self.observation_space = spaces.Box(low=0, high=255,
+                shape=(self.height, self.width, 1), dtype=np.uint8)
         else:
-            num_colors = 3
+            self.observation_space = spaces.Box(low=0, high=255,
+                shape=(self.height, self.width, 3), dtype=np.uint8)
 
-        new_space = gym.spaces.Box(
-            low=0,
-            high=255,
-            shape=(self._height, self._width, num_colors),
-            dtype=np.uint8,
-        )
-        if self._key is None:
-            original_space = self.observation_space
-            self.observation_space = new_space
-        else:
-            original_space = self.observation_space.spaces[self._key]
-            self.observation_space.spaces[self._key] = new_space
-        assert original_space.dtype == np.uint8 and len(original_space.shape) == 3
-
-    def observation(self, obs):
-        if self._key is None:
-            frame = obs
-        else:
-            frame = obs[self._key]
-
-        if self._grayscale:
+    def observation(self, frame):
+        if self.grayscale:
             frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        frame = cv2.resize(
-            frame, (self._width, self._height), interpolation=cv2.INTER_AREA
-        )
-        if self._grayscale:
+        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
+        if self.grayscale:
             frame = np.expand_dims(frame, -1)
-
-        if self._key is None:
-            obs = frame
-        else:
-            obs = obs.copy()
-            obs[self._key] = frame
-        return obs
-
+        return frame
 
 class FrameStack(gym.Wrapper):
     def __init__(self, env, k):

baselines/common/cmd_util.py

@@ -17,26 +17,21 @@ from baselines.common.atari_wrappers import make_atari, wrap_deepmind
 from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
 from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 from baselines.common import retro_wrappers
-from baselines.common.wrappers import ClipActionsWrapper
 
 def make_vec_env(env_id, env_type, num_env, seed,
                  wrapper_kwargs=None,
-                 env_kwargs=None,
                  start_index=0,
                  reward_scale=1.0,
                  flatten_dict_observations=True,
-                 gamestate=None,
-                 initializer=None,
-                 force_dummy=False):
+                 gamestate=None):
     """
     Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
     """
     wrapper_kwargs = wrapper_kwargs or {}
-    env_kwargs = env_kwargs or {}
     mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
     seed = seed + 10000 * mpi_rank if seed is not None else None
     logger_dir = logger.get_dir()
-    def make_thunk(rank, initializer=None):
+    def make_thunk(rank):
         return lambda: make_env(
             env_id=env_id,
             env_type=env_type,
@@ -47,30 +42,18 @@ def make_vec_env(env_id, env_type, num_env, seed,
             gamestate=gamestate,
             flatten_dict_observations=flatten_dict_observations,
             wrapper_kwargs=wrapper_kwargs,
-            env_kwargs=env_kwargs,
-            logger_dir=logger_dir,
-            initializer=initializer
+            logger_dir=logger_dir
         )
 
     set_global_seeds(seed)
-    if not force_dummy and num_env > 1:
-        return SubprocVecEnv([make_thunk(i + start_index, initializer=initializer) for i in range(num_env)])
+    if num_env > 1:
+        return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
     else:
-        return DummyVecEnv([make_thunk(i + start_index, initializer=None) for i in range(num_env)])
+        return DummyVecEnv([make_thunk(start_index)])
 
 
-def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, env_kwargs=None, logger_dir=None, initializer=None):
-    if initializer is not None:
-        initializer(mpi_rank=mpi_rank, subrank=subrank)
-
+def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, logger_dir=None):
     wrapper_kwargs = wrapper_kwargs or {}
-    env_kwargs = env_kwargs or {}
-    if ':' in env_id:
-        import re
-        import importlib
-        module_name = re.sub(':.*','',env_id)
-        env_id = re.sub('.*:', '', env_id)
-        importlib.import_module(module_name)
     if env_type == 'atari':
         env = make_atari(env_id)
     elif env_type == 'retro':
@@ -78,7 +61,7 @@ def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.
         gamestate = gamestate or retro.State.DEFAULT
         env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate)
     else:
-        env = gym.make(env_id, **env_kwargs)
+        env = gym.make(env_id)
 
     if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
         keys = env.observation_space.spaces.keys()
@@ -89,7 +72,6 @@ def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.
                   logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
                   allow_early_resets=True)
 
-
     if env_type == 'atari':
         env = wrap_deepmind(env, **wrapper_kwargs)
     elif env_type == 'retro':
@@ -97,9 +79,6 @@ def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.
             wrapper_kwargs['frame_stack'] = 1
         env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
 
-    if isinstance(env.action_space, gym.spaces.Box):
-        env = ClipActionsWrapper(env)
-
     if reward_scale != 1:
         env = retro_wrappers.RewardScaler(env, reward_scale)
 

baselines/common/distributions.py

@@ -275,133 +275,6 @@ class BernoulliPd(Pd):
     def fromflat(cls, flat):
         return cls(flat)
 
-
-
-def _np_cast(x, dtype):
-    """Numpy cast, equivalent to tf.cast"""
-    return x.astype(dtype)
-
-
-def decode_tuple_sample(pdtypes, x):
-    """
-    Cast and convert a sample from its dense concatenated state back to constituent parts.
-
-    Arguments
-    ---------
-
-    :param pdtypes: list<PdType>, a TuplePdType's child PdTypes.
-    :param x: np.ndarray or tf.Tensor.
-              Shape is [..., sum(pdtype.sample_shape for pdtype in pdtypes)]
-
-    :return output, list<np.ndarray> or list<tf.Tensor>, the split and correctly casted
-            policy samples.
-    """
-    if isinstance(x, np.ndarray):
-        cast_fn = _np_cast
-        numpy_casting = True
-    else:
-        cast_fn = tf.cast
-        numpy_casting = False
-
-    so_far = 0
-    xs = []
-    for pdtype in pdtypes:
-        sample_size = pdtype.sample_shape()[0] if len(pdtype.sample_shape()) > 0 else 1
-        if len(pdtype.sample_shape()) == 0:
-            slided_x = x[..., so_far]
-        else:
-            slided_x = x[..., so_far:so_far + sample_size]
-
-        desired_dtype = pdtype.sample_dtype()
-        if numpy_casting:
-            desired_dtype = desired_dtype.as_numpy_dtype
-        if desired_dtype != x:
-            slided_x = cast_fn(slided_x, desired_dtype)
-        xs.append(slided_x)
-        so_far += sample_size
-    return xs
-
-
-class TuplePd(Pd):
-    def __init__(self, sample_dtype, pdtypes, logits):
-        self.pdtypes = pdtypes
-        self.sample_dtype = sample_dtype
-        self.pds = []
-        so_far = 0
-        for pdtype in self.pdtypes:
-            param_shape = pdtype.param_shape()[0]
-            self.pds.append(pdtype.pdfromflat(logits[..., so_far:so_far + param_shape]))
-            so_far += param_shape
-
-    def flatparam(self):
-        return tf.concat([pd.flatparam() for pd in self.pds], axis=-1)
-
-    def mode(self):
-        return self.tuple_sample_concat([pd.mode() for pd in self.pds])
-
-    def tuple_sample_concat(self, samples):
-        out = []
-        for sample, pdtype in zip(samples, self.pdtypes):
-            if len(pdtype.sample_shape()) == 0:
-                sample = tf.expand_dims(sample, axis=-1)
-            if sample.dtype != self.sample_dtype:
-                sample = tf.cast(sample, self.sample_dtype)
-            out.append(sample)
-        return tf.concat(out, axis=-1)
-
-    def sample(self):
-        return self.tuple_sample_concat([pd.sample() for pd in self.pds])
-
-    def neglogp(self, x):
-        return tf.add_n([pd.neglogp(xi) for pd, xi in zip(self.pds, decode_tuple_sample(self.pdtypes, x))])
-
-    def entropy(self):
-        return tf.add_n([pd.entropy() for pd in self.pds])
-
-
-def _dtype_promotion(old, new):
-    """
-    Find the highest precision common ground between two tensorflow datatypes.
-    if old is None, it is ignored.
-    """
-    if old is None or (new.is_floating and old.is_integer):
-        return new
-    if old.is_floating and old.is_integer:
-        return old
-    if (old.is_floating and new.is_floating) or (new.is_integer and new.is_integer):
-        # take the largest type (e.g. float64 over float32)
-        return old if old.size > new.size else new
-    raise ValueError("No idea how to promote {} and {}.".format(old, new))
-
-
-class TuplePdType(PdType):
-    def __init__(self, space):
-        self.internal_pdtypes = [make_pdtype(space) for space in space.spaces]
-
-    def decode_sample(self, x):
-        return decode_tuple_sample(self.internal_pdtypes, x)
-
-    def pdclass(self):
-        return TuplePd
-
-    def pdfromflat(self, flat):
-        return TuplePd(self.sample_dtype(), self.internal_pdtypes, flat)
-
-    def param_shape(self):
-        return [sum([pdtype.param_shape()[0]
-                     for pdtype in self.internal_pdtypes])]
-
-    def sample_shape(self):
-        return [sum([pdtype.sample_shape()[0] if len(pdtype.sample_shape()) > 0 else 1
-                     for pdtype in self.internal_pdtypes])]
-
-    def sample_dtype(self):
-        dtype = None
-        for pdtype in self.internal_pdtypes:
-            dtype = _dtype_promotion(dtype, pdtype.sample_dtype())
-        return dtype
-
-
 def make_pdtype(ac_space):
     from gym import spaces
     if isinstance(ac_space, spaces.Box):
@@ -413,12 +286,9 @@ def make_pdtype(ac_space):
         return MultiCategoricalPdType(ac_space.nvec)
     elif isinstance(ac_space, spaces.MultiBinary):
         return BernoulliPdType(ac_space.n)
-    elif isinstance(ac_space, spaces.Tuple):
-        return TuplePdType(ac_space)
     else:
         raise NotImplementedError
 
-
 def shape_el(v, i):
     maybe = v.get_shape()[i]
     if maybe is not None:

baselines/common/models.py

@@ -3,6 +3,7 @@ import tensorflow as tf
 from baselines.a2c import utils
 from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch
 from baselines.common.mpi_running_mean_std import RunningMeanStd
+import tensorflow.contrib.layers as layers
 
 mapping = {}
 
@@ -25,51 +26,6 @@ def nature_cnn(unscaled_images, **conv_kwargs):
     h3 = conv_to_fc(h3)
     return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
 
-def build_impala_cnn(unscaled_images, depths=[16,32,32], **conv_kwargs):
-    """
-    Model used in the paper "IMPALA: Scalable Distributed Deep-RL with
-    Importance Weighted Actor-Learner Architectures" https://arxiv.org/abs/1802.01561
-    """
-
-    layer_num = 0
-
-    def get_layer_num_str():
-        nonlocal layer_num
-        num_str = str(layer_num)
-        layer_num += 1
-        return num_str
-
-    def conv_layer(out, depth):
-        return tf.layers.conv2d(out, depth, 3, padding='same', name='layer_' + get_layer_num_str())
-
-    def residual_block(inputs):
-        depth = inputs.get_shape()[-1].value
-
-        out = tf.nn.relu(inputs)
-
-        out = conv_layer(out, depth)
-        out = tf.nn.relu(out)
-        out = conv_layer(out, depth)
-        return out + inputs
-
-    def conv_sequence(inputs, depth):
-        out = conv_layer(inputs, depth)
-        out = tf.layers.max_pooling2d(out, pool_size=3, strides=2, padding='same')
-        out = residual_block(out)
-        out = residual_block(out)
-        return out
-
-    out = tf.cast(unscaled_images, tf.float32) / 255.
-
-    for depth in depths:
-        out = conv_sequence(out, depth)
-
-    out = tf.layers.flatten(out)
-    out = tf.nn.relu(out)
-    out = tf.layers.dense(out, 256, activation=tf.nn.relu, name='layer_' + get_layer_num_str())
-
-    return out
-
 
 @register("mlp")
 def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):
@@ -109,11 +65,6 @@ def cnn(**conv_kwargs):
         return nature_cnn(X, **conv_kwargs)
     return network_fn
 
-@register("impala_cnn")
-def impala_cnn(**conv_kwargs):
-    def network_fn(X):
-        return build_impala_cnn(X)
-    return network_fn
 
 @register("cnn_small")
 def cnn_small(**conv_kwargs):
@@ -128,6 +79,7 @@ def cnn_small(**conv_kwargs):
         return h
     return network_fn
 
+
 @register("lstm")
 def lstm(nlstm=128, layer_norm=False):
     """
@@ -184,12 +136,12 @@ def lstm(nlstm=128, layer_norm=False):
 
 
 @register("cnn_lstm")
-def cnn_lstm(nlstm=128, layer_norm=False, conv_fn=nature_cnn, **conv_kwargs):
+def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
     def network_fn(X, nenv=1):
         nbatch = X.shape[0]
         nsteps = nbatch // nenv
 
-        h = conv_fn(X, **conv_kwargs)
+        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
@@ -209,9 +161,6 @@ def cnn_lstm(nlstm=128, layer_norm=False, conv_fn=nature_cnn, **conv_kwargs):
 
     return network_fn
 
-@register("impala_cnn_lstm")
-def impala_cnn_lstm():
-    return cnn_lstm(nlstm=256, conv_fn=build_impala_cnn)
 
 @register("cnn_lnlstm")
 def cnn_lnlstm(nlstm=128, **conv_kwargs):
@@ -238,7 +187,7 @@ def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
         out = tf.cast(X, tf.float32) / 255.
         with tf.variable_scope("convnet"):
             for num_outputs, kernel_size, stride in convs:
-                out = tf.contrib.layers.convolution2d(out,
+                out = layers.convolution2d(out,
                                            num_outputs=num_outputs,
                                            kernel_size=kernel_size,
                                            stride=stride,

baselines/common/mpi_adam_optimizer.py

@@ -2,7 +2,6 @@ import numpy as np
 import tensorflow as tf
 from baselines.common import tf_util as U
 from baselines.common.tests.test_with_mpi import with_mpi
-from baselines import logger
 try:
     from mpi4py import MPI
 except ImportError:
@@ -10,34 +9,22 @@ except ImportError:
 
 class MpiAdamOptimizer(tf.train.AdamOptimizer):
     """Adam optimizer that averages gradients across mpi processes."""
-    def __init__(self, comm, grad_clip=None, mpi_rank_weight=1, **kwargs):
+    def __init__(self, comm, **kwargs):
         self.comm = comm
-        self.grad_clip = grad_clip
-        self.mpi_rank_weight = mpi_rank_weight
         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) * self.mpi_rank_weight
+        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]
-
-        total_weight = np.zeros(1, np.float32)
-        self.comm.Allreduce(np.array([self.mpi_rank_weight], dtype=np.float32), total_weight, op=MPI.SUM)
-        total_weight = total_weight[0]
-
+        num_tasks = self.comm.Get_size()
         buf = np.zeros(sum(sizes), np.float32)
         countholder = [0] # Counts how many times _collect_grads has been called
         stat = tf.reduce_sum(grads_and_vars[0][1]) # sum of first variable
         def _collect_grads(flat_grad, np_stat):
-            if self.grad_clip is not None:
-                gradnorm = np.linalg.norm(flat_grad)
-                if gradnorm > 1:
-                    flat_grad /= gradnorm
-                logger.logkv_mean('gradnorm', gradnorm)
-                logger.logkv_mean('gradclipfrac', float(gradnorm > 1))
             self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
-            np.divide(buf, float(total_weight), out=buf)
+            np.divide(buf, float(num_tasks), out=buf)
             if countholder[0] % 100 == 0:
                 check_synced(np_stat, self.comm)
             countholder[0] += 1
@@ -64,8 +51,8 @@ def check_synced(localval, comm=None):
     comm = comm or MPI.COMM_WORLD
     vals = comm.gather(localval)
     if comm.rank == 0:
-        assert all(val==vals[0] for val in vals[1:]),\
-            f'MpiAdamOptimizer detected that different workers have different weights: {vals}'
+        assert all(val==vals[0] for val in vals[1:])
+
 
 @with_mpi(timeout=5)
 def test_nonfreeze():
@@ -88,3 +75,4 @@ def test_nonfreeze():
         l,_ = sess.run([loss, update_op])
         print(i, l)
         losslist_ref.append(l)
+

baselines/common/plot_util.py

@@ -90,8 +90,6 @@ def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_
         sum_y *= interstep_decay
         count_y *= interstep_decay
         while True:
-            if luoi >= len(xolds):
-                break
             xold = xolds[luoi]
             if xold <= xnew:
                 decay = np.exp(- (xnew - xold) / decay_period)
@@ -100,6 +98,8 @@ def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_
                 luoi += 1
             else:
                 break
+            if luoi >= len(xolds):
+                break
         sum_ys[i] = sum_y
         count_ys[i] = count_y
 
@@ -248,10 +248,7 @@ def plot_results(
     figsize=None,
     legend_outside=False,
     resample=0,
-    smooth_step=1.0,
-    tiling='vertical',
-    xlabel=None,
-    ylabel=None
+    smooth_step=1.0
 ):
     '''
     Plot multiple Results objects
@@ -303,23 +300,9 @@ def plot_results(
         sk2r[splitkey].append(result)
     assert len(sk2r) > 0
     assert isinstance(resample, int), "0: don't resample. <integer>: that many samples"
-    if tiling == 'vertical' or tiling is None:
-        nrows = len(sk2r)
-        ncols = 1
-    elif tiling == 'horizontal':
-        ncols = len(sk2r)
-        nrows = 1
-    elif tiling == 'symmetric':
-        import math
-        N = len(sk2r)
-        largest_divisor = 1
-        for i in range(1, int(math.sqrt(N))+1):
-            if N % i == 0:
-                largest_divisor = i
-        ncols = largest_divisor
-        nrows = N // ncols
-    figsize = figsize or (6 * ncols, 6 * nrows)
-
+    nrows = len(sk2r)
+    ncols = 1
+    figsize = figsize or (6, 6 * nrows)
     f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize)
 
     groups = list(set(group_fn(result) for result in allresults))
@@ -333,9 +316,7 @@ def plot_results(
         g2c = defaultdict(int)
         sresults = sk2r[sk]
         gresults = defaultdict(list)
-        idx_row = isplit // ncols
-        idx_col = isplit % ncols
-        ax = axarr[idx_row][idx_col]
+        ax = axarr[isplit][0]
         for result in sresults:
             group = group_fn(result)
             g2c[group] += 1
@@ -374,7 +355,7 @@ def plot_results(
                 ymean = np.mean(ys, axis=0)
                 ystd = np.std(ys, axis=0)
                 ystderr = ystd / np.sqrt(len(ys))
-                l, = axarr[idx_row][idx_col].plot(usex, ymean, color=color)
+                l, = axarr[isplit][0].plot(usex, ymean, color=color)
                 g2l[group] = l
                 if shaded_err:
                     ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4)
@@ -391,17 +372,6 @@ def plot_results(
                 loc=2 if legend_outside else None,
                 bbox_to_anchor=(1,1) if legend_outside else None)
         ax.set_title(sk)
-        # add xlabels, but only to the bottom row
-        if xlabel is not None:
-            for ax in axarr[-1]:
-                plt.sca(ax)
-                plt.xlabel(xlabel)
-        # add ylabels, but only to left column
-        if ylabel is not None:
-            for ax in axarr[:,0]:
-                plt.sca(ax)
-                plt.ylabel(ylabel)
-
     return f, axarr
 
 def regression_analysis(df):

baselines/common/test_mpi_util.py

@@ -1,13 +1,10 @@
-from baselines.common import mpi_util
 from baselines import logger
 from baselines.common.tests.test_with_mpi import with_mpi
-try:
-    from mpi4py import MPI
-except ImportError:
-    MPI = None
+from baselines.common import mpi_util
 
 @with_mpi()
 def test_mpi_weighted_mean():
+    from mpi4py import MPI
     comm = MPI.COMM_WORLD
     with logger.scoped_configure(comm=comm):
         if comm.rank == 0:
@@ -16,6 +13,7 @@ def test_mpi_weighted_mean():
             name2valcount = {'a' : (19, 1), 'c' : (42,3)}
         else:
             raise NotImplementedError
+
         d = mpi_util.mpi_weighted_mean(comm, name2valcount)
         correctval = {'a' : (10 * 2 + 19) / 3.0, 'b' : 20, 'c' : 42}
         if comm.rank == 0:

baselines/common/tests/__init__.py

@@ -1,2 +0,0 @@
-import os, pytest
-mark_slow = pytest.mark.skipif(not os.getenv('RUNSLOW'), reason='slow')

baselines/common/tests/envs/fixed_sequence_env.py

@@ -9,16 +9,18 @@ class FixedSequenceEnv(Env):
             n_actions=10,
             episode_len=100
     ):
+        self.np_random = np.random.RandomState()
+        self.sequence = None
+
         self.action_space = Discrete(n_actions)
         self.observation_space = Discrete(1)
-        self.np_random = np.random.RandomState(0)
+
         self.episode_len = episode_len
-        self.sequence = [self.np_random.randint(0, self.action_space.n)
-            for _ in range(self.episode_len)]
         self.time = 0
 
-
     def reset(self):
+        if self.sequence is None:
+            self.sequence = [self.np_random.randint(0, self.action_space.n-1) for _ in range(self.episode_len)]
         self.time = 0
         return 0
 
@@ -27,6 +29,7 @@ class FixedSequenceEnv(Env):
         self._choose_next_state()
         done = False
         if self.episode_len and self.time >= self.episode_len:
+            rew = 0
             done = True
 
         return 0, rew, done, {}

baselines/common/tests/envs/identity_env.py

@@ -2,45 +2,43 @@ import numpy as np
 from abc import abstractmethod
 from gym import Env
 from gym.spaces import MultiDiscrete, Discrete, Box
-from collections import deque
+
 
 class IdentityEnv(Env):
     def __init__(
             self,
-            episode_len=None,
-            delay=0,
-            zero_first_rewards=True
+            episode_len=None
     ):
 
         self.observation_space = self.action_space
         self.episode_len = episode_len
         self.time = 0
-        self.delay = delay
-        self.zero_first_rewards = zero_first_rewards
-        self.q = deque(maxlen=delay+1)
+        self.reset()
 
     def reset(self):
-        self.q.clear()
-        for _ in range(self.delay + 1):
-            self.q.append(self.action_space.sample())
+        self._choose_next_state()
         self.time = 0
 
-        return self.q[-1]
+        return self.state
 
     def step(self, actions):
-        rew = self._get_reward(self.q.popleft(), actions)
-        if self.zero_first_rewards and self.time < self.delay:
-            rew = 0
-        self.q.append(self.action_space.sample())
-        self.time += 1
-        done = self.episode_len is not None and self.time >= self.episode_len
-        return self.q[-1], rew, done, {}
+        rew = self._get_reward(actions)
+        self._choose_next_state()
+        done = False
+        if self.episode_len and self.time >= self.episode_len:
+            done = True
+
+        return self.state, rew, done, {}
 
     def seed(self, seed=None):
         self.action_space.seed(seed)
 
+    def _choose_next_state(self):
+        self.state = self.action_space.sample()
+        self.time += 1
+
     @abstractmethod
-    def _get_reward(self, state, actions):
+    def _get_reward(self, actions):
         raise NotImplementedError
 
 
@@ -49,29 +47,26 @@ class DiscreteIdentityEnv(IdentityEnv):
             self,
             dim,
             episode_len=None,
-            delay=0,
-            zero_first_rewards=True
     ):
 
         self.action_space = Discrete(dim)
-        super().__init__(episode_len=episode_len, delay=delay, zero_first_rewards=zero_first_rewards)
+        super().__init__(episode_len=episode_len)
 
-    def _get_reward(self, state, actions):
-        return 1 if state == actions else 0
+    def _get_reward(self, actions):
+        return 1 if self.state == actions else 0
 
 class MultiDiscreteIdentityEnv(IdentityEnv):
     def __init__(
             self,
             dims,
             episode_len=None,
-            delay=0,
     ):
 
         self.action_space = MultiDiscrete(dims)
-        super().__init__(episode_len=episode_len, delay=delay)
+        super().__init__(episode_len=episode_len)
 
-    def _get_reward(self, state, actions):
-        return 1 if all(state == actions) else 0
+    def _get_reward(self, actions):
+        return 1 if all(self.state == actions) else 0
 
 
 class BoxIdentityEnv(IdentityEnv):
@@ -84,7 +79,7 @@ class BoxIdentityEnv(IdentityEnv):
         self.action_space = Box(low=-1.0, high=1.0, shape=shape, dtype=np.float32)
         super().__init__(episode_len=episode_len)
 
-    def _get_reward(self, state, actions):
-        diff = actions - state
+    def _get_reward(self, actions):
+        diff = actions - self.state
         diff = diff[:]
         return -0.5 * np.dot(diff, diff)

baselines/common/tests/envs/identity_env_test.py

@@ -1,36 +0,0 @@
-from baselines.common.tests.envs.identity_env import DiscreteIdentityEnv
-
-
-def test_discrete_nodelay():
-    nsteps = 100
-    eplen = 50
-    env = DiscreteIdentityEnv(10, episode_len=eplen)
-    ob = env.reset()
-    for t in range(nsteps):
-        action = env.action_space.sample()
-        next_ob, rew, done, info = env.step(action)
-        assert rew == (1 if action == ob else 0)
-        if (t + 1) % eplen == 0:
-            assert done
-            next_ob = env.reset()
-        else:
-            assert not done
-        ob = next_ob
-
-def test_discrete_delay1():
-    eplen = 50
-    env = DiscreteIdentityEnv(10, episode_len=eplen, delay=1)
-    ob = env.reset()
-    prev_ob = None
-    for t in range(eplen):
-        action = env.action_space.sample()
-        next_ob, rew, done, info = env.step(action)
-        if t > 0:
-            assert rew == (1 if action == prev_ob else 0)
-        else:
-            assert rew == 0
-        prev_ob = ob
-        ob = next_ob
-        if t < eplen - 1:
-            assert not done
-    assert done

baselines/common/tests/test_cartpole.py

@@ -3,7 +3,6 @@ import gym
 
 from baselines.run import get_learn_function
 from baselines.common.tests.util import reward_per_episode_test
-from baselines.common.tests import mark_slow
 
 common_kwargs = dict(
     total_timesteps=30000,
@@ -21,7 +20,7 @@ learn_kwargs = {
     'trpo_mpi': {}
 }
 
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", learn_kwargs.keys())
 def test_cartpole(alg):
     '''

baselines/common/tests/test_fetchreach.py

@@ -3,7 +3,6 @@ import gym
 
 from baselines.run import get_learn_function
 from baselines.common.tests.util import reward_per_episode_test
-from baselines.common.tests import mark_slow
 
 pytest.importorskip('mujoco_py')
 
@@ -16,7 +15,7 @@ learn_kwargs = {
     'her': dict(total_timesteps=2000)
 }
 
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", learn_kwargs.keys())
 def test_fetchreach(alg):
     '''

baselines/common/tests/test_fixed_sequence.py

@@ -3,8 +3,6 @@ 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
-from baselines.common.tests import mark_slow
-
 
 common_kwargs = dict(
     seed=0,
@@ -19,11 +17,11 @@ learn_kwargs = {
     # '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']
 
-@mark_slow
+
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", alg_list)
 @pytest.mark.parametrize("rnn", rnn_list)
 def test_fixed_sequence(alg, rnn):
@@ -35,6 +33,9 @@ def test_fixed_sequence(alg, rnn):
     kwargs = learn_kwargs[alg]
     kwargs.update(common_kwargs)
 
+    if alg == 'ppo2' and rnn.endswith('lstm'):
+        rnn = 'ppo_' + rnn
+
     env_fn = lambda: FixedSequenceEnv(n_actions=10, episode_len=5)
     learn = lambda e: get_learn_function(alg)(
         env=e,
@@ -47,6 +48,3 @@ def test_fixed_sequence(alg, rnn):
 
 if __name__ == '__main__':
     test_fixed_sequence('ppo2', 'lstm')
-
-
-

baselines/common/tests/test_identity.py

@@ -2,7 +2,6 @@ import pytest
 from baselines.common.tests.envs.identity_env import DiscreteIdentityEnv, BoxIdentityEnv, MultiDiscreteIdentityEnv
 from baselines.run import get_learn_function
 from baselines.common.tests.util import simple_test
-from baselines.common.tests import mark_slow
 
 common_kwargs = dict(
     total_timesteps=30000,
@@ -25,7 +24,7 @@ algos_disc = ['a2c', 'acktr', 'deepq', 'ppo2', 'trpo_mpi']
 algos_multidisc = ['a2c', 'acktr', 'ppo2', 'trpo_mpi']
 algos_cont = ['a2c', 'acktr', 'ddpg',  'ppo2', 'trpo_mpi']
 
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", algos_disc)
 def test_discrete_identity(alg):
     '''
@@ -40,7 +39,7 @@ def test_discrete_identity(alg):
     env_fn = lambda: DiscreteIdentityEnv(10, episode_len=100)
     simple_test(env_fn, learn_fn, 0.9)
 
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", algos_multidisc)
 def test_multidiscrete_identity(alg):
     '''
@@ -55,7 +54,7 @@ def test_multidiscrete_identity(alg):
     env_fn = lambda: MultiDiscreteIdentityEnv((3,3), episode_len=100)
     simple_test(env_fn, learn_fn, 0.9)
 
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", algos_cont)
 def test_continuous_identity(alg):
     '''

baselines/common/tests/test_mnist.py

@@ -4,7 +4,7 @@ import pytest
 from baselines.common.tests.envs.mnist_env import MnistEnv
 from baselines.common.tests.util import simple_test
 from baselines.run import get_learn_function
-from baselines.common.tests import mark_slow
+
 
 # TODO investigate a2c and ppo2 failures - is it due to bad hyperparameters for this problem?
 # GitHub issue https://github.com/openai/baselines/issues/189
@@ -28,7 +28,7 @@ learn_args = {
 #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
-@mark_slow
+@pytest.mark.slow
 @pytest.mark.parametrize("alg", learn_args.keys())
 def test_mnist(alg):
     '''

baselines/common/tests/test_plot_util.py

@@ -1,17 +0,0 @@
-# smoke tests of plot_util
-from baselines.common import plot_util as pu
-from baselines.common.tests.util import smoketest
-
-
-def test_plot_util():
-    nruns = 4
-    logdirs = [smoketest('--alg=ppo2 --env=CartPole-v0 --num_timesteps=10000') for _ in range(nruns)]
-    data = pu.load_results(logdirs)
-    assert len(data) == 4
-
-    _, axes = pu.plot_results(data[:1]); assert len(axes) == 1
-    _, axes = pu.plot_results(data, tiling='vertical'); assert axes.shape==(4,1)
-    _, axes = pu.plot_results(data, tiling='horizontal'); assert axes.shape==(1,4)
-    _, axes = pu.plot_results(data, tiling='symmetric'); assert axes.shape==(2,2)
-    _, axes = pu.plot_results(data, split_fn=lambda _: ''); assert len(axes) == 1
-

baselines/common/tests/test_serialization.py

@@ -1,17 +1,16 @@
 import os
-import gym
 import tempfile
-import pytest
-import tensorflow as tf
-import numpy as np
-
-from baselines.common.tests.envs.mnist_env import MnistEnv
-from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-from baselines.run import get_learn_function
-from baselines.common.tf_util import make_session, get_session
-
 from functools import partial
 
+import gym
+import numpy as np
+import pytest
+import tensorflow as tf
+
+from baselines.common.tests.envs.mnist_env import MnistEnv
+from baselines.common.tf_util import make_session, get_session
+from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.run import get_learn_function
 
 learn_kwargs = {
     'deepq': {},
@@ -37,12 +36,15 @@ def test_serialization(learn_fn, network_fn):
     Test if the trained model can be serialized
     '''
 
+    _network_kwargs = network_kwargs[network_fn]
 
     if network_fn.endswith('lstm') and learn_fn in ['acer', 'acktr', 'trpo_mpi', 'deepq']:
-            # TODO make acktr work with recurrent policies
-            # and test
-            # github issue: https://github.com/openai/baselines/issues/660
-            return
+        # TODO make acktr work with recurrent policies
+        # and test
+        # github issue: https://github.com/openai/baselines/issues/660
+        return
+    elif network_fn.endswith('lstm') and learn_fn == 'ppo2':
+        network_fn = 'ppo_' + network_fn
 
     def make_env():
         env = MnistEnv(episode_len=100)
@@ -54,10 +56,9 @@ def test_serialization(learn_fn, network_fn):
     learn = get_learn_function(learn_fn)
 
     kwargs = {}
-    kwargs.update(network_kwargs[network_fn])
+    kwargs.update(_network_kwargs)
     kwargs.update(learn_kwargs[learn_fn])
 
-
     learn = partial(learn, env=env, network=network_fn, seed=0, **kwargs)
 
     with tempfile.TemporaryDirectory() as td:
@@ -76,7 +77,7 @@ def test_serialization(learn_fn, network_fn):
 
         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))
+                                       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)
@@ -90,15 +91,15 @@ def test_coexistence(learn_fn, network_fn):
     '''
 
     if learn_fn == 'deepq':
-            # TODO enable multiple DQN models to be useable at the same time
-            # github issue https://github.com/openai/baselines/issues/656
-            return
+        # TODO enable multiple DQN models to be useable at the same time
+        # github issue https://github.com/openai/baselines/issues/656
+        return
 
     if network_fn.endswith('lstm') and learn_fn in ['acktr', 'trpo_mpi', 'deepq']:
-            # TODO make acktr work with recurrent policies
-            # and test
-            # github issue: https://github.com/openai/baselines/issues/660
-            return
+        # TODO make acktr work with recurrent policies
+        # and test
+        # github issue: https://github.com/openai/baselines/issues/660
+        return
 
     env = DummyVecEnv([lambda: gym.make('CartPole-v0')])
     learn = get_learn_function(learn_fn)
@@ -107,7 +108,7 @@ def test_coexistence(learn_fn, network_fn):
     kwargs.update(network_kwargs[network_fn])
     kwargs.update(learn_kwargs[learn_fn])
 
-    learn =  partial(learn, env=env, network=network_fn, total_timesteps=0, **kwargs)
+    learn = partial(learn, env=env, network=network_fn, total_timesteps=0, **kwargs)
     make_session(make_default=True, graph=tf.Graph())
     model1 = learn(seed=1)
     make_session(make_default=True, graph=tf.Graph())
@@ -117,7 +118,6 @@ def test_coexistence(learn_fn, network_fn):
     model2.step(env.observation_space.sample())
 
 
-
 def _serialize_variables():
     sess = get_session()
     variables = tf.trainable_variables()
@@ -136,4 +136,3 @@ def _get_action_stats(model, ob):
     std = np.std(actions, axis=0)
 
     return mean, std
-

baselines/common/tests/test_with_mpi.py

@@ -4,7 +4,6 @@ import subprocess
 import cloudpickle
 import base64
 import pytest
-from functools import wraps
 
 try:
     from mpi4py import MPI
@@ -13,7 +12,6 @@ except ImportError:
 
 def with_mpi(nproc=2, timeout=30, skip_if_no_mpi=True):
     def outer_thunk(fn):
-        @wraps(fn)
         def thunk(*args, **kwargs):
             serialized_fn = base64.b64encode(cloudpickle.dumps(lambda: fn(*args, **kwargs)))
             subprocess.check_call([

baselines/common/tests/util.py

@@ -5,12 +5,6 @@ from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 N_TRIALS = 10000
 N_EPISODES = 100
 
-_sess_config = tf.ConfigProto(
-    allow_soft_placement=True,
-    intra_op_parallelism_threads=1,
-    inter_op_parallelism_threads=1
-)
-
 def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
     def seeded_env_fn():
         env = env_fn()
@@ -19,7 +13,7 @@ def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
 
     np.random.seed(0)
     env = DummyVecEnv([seeded_env_fn])
-    with tf.Graph().as_default(), tf.Session(config=_sess_config).as_default():
+    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
@@ -40,7 +34,7 @@ def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=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=_sess_config).as_default():
+    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)
@@ -77,16 +71,3 @@ def rollout(env, model, n_trials):
         observations.append(episode_obs)
     return observations, actions, rewards
 
-
-def smoketest(argstr, **kwargs):
-    import tempfile
-    import subprocess
-    import os
-    argstr = 'python -m baselines.run ' + argstr
-    for key, value in kwargs:
-        argstr += ' --{}={}'.format(key, value)
-    tempdir = tempfile.mkdtemp()
-    env = os.environ.copy()
-    env['OPENAI_LOGDIR'] = tempdir
-    subprocess.run(argstr.split(' '), env=env)
-    return tempdir

baselines/common/vec_env/vec_env.py

@@ -145,7 +145,8 @@ class VecEnvWrapper(VecEnv):
 
     def __init__(self, venv, observation_space=None, action_space=None):
         self.venv = venv
-        super().__init__(num_envs=venv.num_envs,
+        VecEnv.__init__(self,
+                        num_envs=venv.num_envs,
                         observation_space=observation_space or venv.observation_space,
                         action_space=action_space or venv.action_space)
 
@@ -169,11 +170,6 @@ class VecEnvWrapper(VecEnv):
     def get_images(self):
         return self.venv.get_images()
 
-    def __getattr__(self, name):
-        if name.startswith('_'):
-            raise AttributeError("attempted to get missing private attribute '{}'".format(name))
-        return getattr(self.venv, name)
-
 class VecEnvObservationWrapper(VecEnvWrapper):
     @abstractmethod
     def process(self, obs):

baselines/common/vec_env/vec_monitor.py

@@ -5,18 +5,16 @@ import time
 from collections import deque
 
 class VecMonitor(VecEnvWrapper):
-    def __init__(self, venv, filename=None, keep_buf=0, info_keywords=()):
+    def __init__(self, venv, filename=None, keep_buf=0):
         VecEnvWrapper.__init__(self, venv)
         self.eprets = None
         self.eplens = None
         self.epcount = 0
         self.tstart = time.time()
         if filename:
-            self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart},
-                extra_keys=info_keywords)
+            self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
         else:
             self.results_writer = None
-        self.info_keywords = info_keywords
         self.keep_buf = keep_buf
         if self.keep_buf:
             self.epret_buf = deque([], maxlen=keep_buf)
@@ -32,16 +30,11 @@ class VecMonitor(VecEnvWrapper):
         obs, rews, dones, infos = self.venv.step_wait()
         self.eprets += rews
         self.eplens += 1
-
-        newinfos = list(infos[:])
-        for i in range(len(dones)):
-            if dones[i]:
-                info = infos[i].copy()
-                ret = self.eprets[i]
-                eplen = self.eplens[i]
+        newinfos = []
+        for (i, (done, ret, eplen, info)) in enumerate(zip(dones, self.eprets, self.eplens, infos)):
+            info = info.copy()
+            if done:
                 epinfo = {'r': ret, 'l': eplen, 't': round(time.time() - self.tstart, 6)}
-                for k in self.info_keywords:
-                    epinfo[k] = info[k]
                 info['episode'] = epinfo
                 if self.keep_buf:
                     self.epret_buf.append(ret)
@@ -51,5 +44,6 @@ class VecMonitor(VecEnvWrapper):
                 self.eplens[i] = 0
                 if self.results_writer:
                     self.results_writer.write_row(epinfo)
-                newinfos[i] = info
+            newinfos.append(info)
+
         return obs, rews, dones, newinfos

baselines/common/vec_env/vec_normalize.py

@@ -1,22 +1,18 @@
 from . import VecEnvWrapper
+from baselines.common.running_mean_std import RunningMeanStd
 import numpy as np
 
+
 class VecNormalize(VecEnvWrapper):
     """
     A vectorized wrapper that normalizes the observations
     and returns from an environment.
     """
 
-    def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8, use_tf=False):
+    def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
         VecEnvWrapper.__init__(self, venv)
-        if use_tf:
-            from baselines.common.running_mean_std import TfRunningMeanStd
-            self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='ob_rms') if ob else None
-            self.ret_rms = TfRunningMeanStd(shape=(), scope='ret_rms') if ret else None
-        else:
-            from baselines.common.running_mean_std import RunningMeanStd
-            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 = RunningMeanStd(shape=self.observation_space.shape) if ob else None
+        self.ret_rms = RunningMeanStd(shape=()) if ret else None
         self.clipob = clipob
         self.cliprew = cliprew
         self.ret = np.zeros(self.num_envs)

baselines/common/vec_env/vec_remove_dict_obs.py

@@ -1,5 +1,6 @@
 from .vec_env import VecEnvObservationWrapper
 
+
 class VecExtractDictObs(VecEnvObservationWrapper):
     def __init__(self, venv, key):
         self.key = key
@@ -7,4 +8,4 @@ class VecExtractDictObs(VecEnvObservationWrapper):
             observation_space=venv.observation_space.spaces[self.key])
 
     def process(self, obs):
-        return obs[self.key]
+        return obs[self.key]

baselines/common/wrappers.py

@@ -16,14 +16,4 @@ class TimeLimit(gym.Wrapper):
 
     def reset(self, **kwargs):
         self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
-
-class ClipActionsWrapper(gym.Wrapper):
-    def step(self, action):
-        import numpy as np
-        action = np.nan_to_num(action)
-        action = np.clip(action, self.action_space.low, self.action_space.high)
-        return self.env.step(action)
-
-    def reset(self, **kwargs):
-        return self.env.reset(**kwargs)
+        return self.env.reset(**kwargs)

baselines/ddpg/ddpg.py

@@ -217,9 +217,7 @@ def learn(network, env,
         stats = agent.get_stats()
         combined_stats = stats.copy()
         combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
-        combined_stats['rollout/return_std'] = np.std(epoch_episode_rewards)
         combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
-        combined_stats['rollout/return_history_std'] = np.std(episode_rewards_history)
         combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
         combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
         combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)

baselines/ddpg/test_smoke.py

@@ -1,6 +1,10 @@
-from baselines.common.tests.util import smoketest
+from multiprocessing import Process
+import baselines.run
+
 def _run(argstr):
-    smoketest('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr)
+    p = Process(target=baselines.run.main, args=('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr).split(' '))
+    p.start()
+    p.join()
 
 def test_popart():
     _run('--normalize_returns=True --popart=True')

baselines/logger.py

@@ -38,8 +38,8 @@ class HumanOutputFormat(KVWriter, SeqWriter):
         # Create strings for printing
         key2str = {}
         for (key, val) in sorted(kvs.items()):
-            if hasattr(val, '__float__'):
-                valstr = '%-8.3g' % val
+            if isinstance(val, float):
+                valstr = '%-8.3g' % (val,)
             else:
                 valstr = str(val)
             key2str[self._truncate(key)] = self._truncate(valstr)
@@ -92,6 +92,7 @@ class JSONOutputFormat(KVWriter):
     def writekvs(self, kvs):
         for k, v in sorted(kvs.items()):
             if hasattr(v, 'dtype'):
+                v = v.tolist()
                 kvs[k] = float(v)
         self.file.write(json.dumps(kvs) + '\n')
         self.file.flush()
@@ -360,16 +361,7 @@ class Logger(object):
             if isinstance(fmt, SeqWriter):
                 fmt.writeseq(map(str, args))
 
-def get_rank_without_mpi_import():
-    # check environment variables here instead of importing mpi4py
-    # to avoid calling MPI_Init() when this module is imported
-    for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']:
-        if varname in os.environ:
-            return int(os.environ[varname])
-    return 0
-
-
-def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
+def configure(dir=None, format_strs=None, comm=None):
     """
     If comm is provided, average all numerical stats across that comm
     """
@@ -381,9 +373,15 @@ def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
     assert isinstance(dir, str)
     os.makedirs(dir, exist_ok=True)
 
-    rank = get_rank_without_mpi_import()
+    log_suffix = ''
+    rank = 0
+    # check environment variables here instead of importing mpi4py
+    # to avoid calling MPI_Init() when this module is imported
+    for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']:
+        if varname in os.environ:
+            rank = int(os.environ[varname])
     if rank > 0:
-        log_suffix = log_suffix + "-rank%03i" % rank
+        log_suffix = "-rank%03i" % rank
 
     if format_strs is None:
         if rank == 0:

baselines/ppo2/README.md

@@ -3,6 +3,16 @@
 - Original paper: https://arxiv.org/abs/1707.06347
 - Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
 
+## Examples 
 - `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)
+
+### RNN networks
+- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --network=ppo_cnn_lstm` runs on an Atari Pong with 
+    `ppo_cnn_lstm` network.
+- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6 --network=ppo_lstm --value_network=copy` 
+    runs on a Mujoco Ant environment with `ppo_lstm` network whose value and policy networks are separated, but have 
+    same structure.
+
+## See Also
+- refer to the repo-wide [README.md](../../README.md#training-models)

baselines/ppo2/__init__.py

@@ -0,0 +1 @@
+from baselines.ppo2.layers import ppo_lstm, ppo_cnn_lstm, ppo_cnn_lnlstm  # pylint: disable=unused-import # noqa: F401

baselines/ppo2/layers.py

@@ -0,0 +1,55 @@
+import numpy as np
+import tensorflow as tf
+
+from baselines.a2c.utils import ortho_init, lstm, lnlstm
+from baselines.common.models import register, nature_cnn
+
+
+class RNN(object):
+    def __init__(self, func, memory_size=None):
+        self._func = func
+        self.memory_size = memory_size
+
+    def __call__(self, *args, **kwargs):
+        return self._func(*args, **kwargs)
+
+
+@register("ppo_lstm")
+def ppo_lstm(num_units=128, layer_norm=False):
+    def network_fn(input, mask, state):
+        input = tf.layers.flatten(input)
+        mask = tf.to_float(mask)
+
+        if layer_norm:
+            h, next_state = lnlstm([input], [mask[:, None]], state, scope='lnlstm', nh=num_units)
+        else:
+            h, next_state = lstm([input], [mask[:, None]], state, scope='lstm', nh=num_units)
+        h = h[0]
+        return h, next_state
+
+    return RNN(network_fn, memory_size=num_units * 2)
+
+
+@register("ppo_cnn_lstm")
+def ppo_cnn_lstm(num_units=128, layer_norm=False, **conv_kwargs):
+    def network_fn(input, mask, state):
+        mask = tf.to_float(mask)
+        initializer = ortho_init(np.sqrt(2))
+
+        h = nature_cnn(input, **conv_kwargs)
+        h = tf.layers.flatten(h)
+        h = tf.layers.dense(h, units=512, activation=tf.nn.relu, kernel_initializer=initializer)
+
+        if layer_norm:
+            h, next_state = lnlstm([h], [mask[:, None]], state, scope='lnlstm', nh=num_units)
+        else:
+            h, next_state = lstm([h], [mask[:, None]], state, scope='lstm', nh=num_units)
+        h = h[0]
+        return h, next_state
+
+    return RNN(network_fn, memory_size=num_units * 2)
+
+
+@register("ppo_cnn_lnlstm")
+def ppo_cnn_lnlstm(num_units=128, **conv_kwargs):
+    return ppo_cnn_lstm(num_units, layer_norm=True, **conv_kwargs)

baselines/ppo2/microbatched_model.py

@@ -1,44 +1,47 @@
-import tensorflow as tf
 import numpy as np
+import tensorflow as tf
 from baselines.ppo2.model import Model
 
+
 class MicrobatchedModel(Model):
     """
     Model that does training one microbatch at a time - when gradient computation
     on the entire minibatch causes some overflow
     """
+
     def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
-                nsteps, ent_coef, vf_coef, max_grad_norm, mpi_rank_weight, comm, microbatch_size):
+                 nsteps, ent_coef, vf_coef, max_grad_norm, microbatch_size):
 
         self.nmicrobatches = nbatch_train // microbatch_size
         self.microbatch_size = microbatch_size
-        assert nbatch_train % microbatch_size == 0, 'microbatch_size ({}) should divide nbatch_train ({}) evenly'.format(microbatch_size, nbatch_train)
+        assert nbatch_train % microbatch_size == 0, 'microbatch_size ({}) should divide nbatch_train ({}) evenly'.format(
+            microbatch_size, nbatch_train)
 
         super().__init__(
-                policy=policy,
-                ob_space=ob_space,
-                ac_space=ac_space,
-                nbatch_act=nbatch_act,
-                nbatch_train=microbatch_size,
-                nsteps=nsteps,
-                ent_coef=ent_coef,
-                vf_coef=vf_coef,
-                max_grad_norm=max_grad_norm,
-                mpi_rank_weight=mpi_rank_weight,
-                comm=comm)
+            policy=policy,
+            ob_space=ob_space,
+            ac_space=ac_space,
+            nbatch_act=nbatch_act,
+            nbatch_train=microbatch_size,
+            nsteps=nsteps,
+            ent_coef=ent_coef,
+            vf_coef=vf_coef,
+            max_grad_norm=max_grad_norm)
 
         self.grads_ph = [tf.placeholder(dtype=g.dtype, shape=g.shape) for g in self.grads]
         grads_ph_and_vars = list(zip(self.grads_ph, self.var))
         self._apply_gradients_op = self.trainer.apply_gradients(grads_ph_and_vars)
 
-
-    def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):
-        assert states is None, "microbatches with recurrent models are not supported yet"
-
-        # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
-        # Returns = R + yV(s')
-        advs = returns - values
-
+    def train(self,
+              lr,
+              cliprange,
+              observations,
+              advs,
+              returns,
+              actions,
+              values,
+              neglogpacs,
+              **_kwargs):
         # Normalize the advantages
         advs = (advs - advs.mean()) / (advs.std() + 1e-8)
 
@@ -46,19 +49,24 @@ class MicrobatchedModel(Model):
         stats_vs = []
 
         for microbatch_idx in range(self.nmicrobatches):
-            _sli = range(microbatch_idx * self.microbatch_size, (microbatch_idx+1) * self.microbatch_size)
+            _sli = range(microbatch_idx * self.microbatch_size, (microbatch_idx + 1) * self.microbatch_size)
+
             td_map = {
-                self.train_model.X: obs[_sli],
-                self.A:actions[_sli],
-                self.ADV:advs[_sli],
-                self.R:returns[_sli],
-                self.CLIPRANGE:cliprange,
-                self.OLDNEGLOGPAC:neglogpacs[_sli],
-                self.OLDVPRED:values[_sli]
+                self.train_model.X: observations[_sli],
+                self.A: actions[_sli],
+                self.ADV: advs[_sli],
+                self.RETURNS: returns[_sli],
+                self.LR: lr,
+                self.CLIPRANGE: cliprange,
+                self.OLDNEGLOGPAC: neglogpacs[_sli],
+                self.VALUE_PREV: values[_sli],
             }
 
+            sliced_kwargs = {key: _kwargs[key][_sli] for key in _kwargs}
+            td_map.update(self.train_model.feed_dict(**sliced_kwargs))
+
             # Compute gradient on a microbatch (note that variables do not change here) ...
-            grad_v, stats_v  = self.sess.run([self.grads, self.stats_list], td_map)
+            grad_v, stats_v = self.sess.run([self.grads, self.stats_list], td_map)
             if microbatch_idx == 0:
                 sum_grad_v = grad_v
             else:
@@ -73,6 +81,3 @@ class MicrobatchedModel(Model):
         self.sess.run(self._apply_gradients_op, feed_dict)
         # Return average of the stats
         return np.mean(np.array(stats_vs), axis=0).tolist()
-
-
-

baselines/ppo2/model.py

@@ -1,8 +1,8 @@
-import tensorflow as tf
 import functools
 
+import tensorflow as tf
+
 from baselines.common.tf_util import get_session, save_variables, load_variables
-from baselines.common.tf_util import initialize
 
 try:
     from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
@@ -11,6 +11,7 @@ try:
 except ImportError:
     MPI = None
 
+
 class Model(object):
     """
     We use this object to :
@@ -24,136 +25,157 @@ class Model(object):
     save/load():
     - Save load the model
     """
+
     def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
-                nsteps, ent_coef, vf_coef, max_grad_norm, mpi_rank_weight=1, comm=None, microbatch_size=None):
-        self.sess = sess = get_session()
+                 nsteps, ent_coef, vf_coef, max_grad_norm,
+                 name='ppo_model',
+                 sess=None,
+                 microbatch_size=None):
+        if sess is None:
+            sess = get_session()
+        self.sess = sess
+        self.name = name
 
-        if MPI is not None and comm is None:
-            comm = MPI.COMM_WORLD
+        with tf.variable_scope(name) as scope:
+            self.scope = scope
+            with tf.variable_scope('models', reuse=tf.AUTO_REUSE):
+                with tf.name_scope('act_model'):
+                    # CREATE OUR TWO MODELS
+                    # act_model that is used for sampling
+                    act_model = policy(nbatch_act, 1, sess)
 
-        with tf.variable_scope('ppo2_model', reuse=tf.AUTO_REUSE):
-            # CREATE OUR TWO MODELS
-            # act_model that is used for sampling
-            act_model = policy(nbatch_act, 1, sess)
+                with tf.name_scope('train_model'):
+                    # Train model for training
+                    if microbatch_size is None:
+                        train_model = policy(nbatch_train, nsteps, sess)
+                    else:
+                        train_model = policy(microbatch_size, nsteps, sess)
 
-            # Train model for training
-            if microbatch_size is None:
-                train_model = policy(nbatch_train, nsteps, sess)
-            else:
-                train_model = policy(microbatch_size, nsteps, sess)
+            with tf.variable_scope('losses'):
+                # CREATE THE PLACEHOLDERS
+                self.A = A = train_model.pdtype.sample_placeholder([None], name='action')
+                self.ADV = ADV = tf.placeholder(tf.float32, [None], name='advantage')
+                self.RETURNS = RETURNS = tf.placeholder(tf.float32, [None], name='reward')
+                self.VALUE_PREV = VALUE_PREV = tf.placeholder(tf.float32, [None], name='value_prev')
+                self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.placeholder(tf.float32, [None],
+                                                                  name='negative_log_p_action_old')
+                self.CLIPRANGE = CLIPRANGE = tf.placeholder(tf.float32, [], name='clip_range')
 
-        # CREATE THE PLACEHOLDERS
-        self.A = A = train_model.pdtype.sample_placeholder([None])
-        self.ADV = ADV = tf.placeholder(tf.float32, [None])
-        self.R = R = tf.placeholder(tf.float32, [None])
-        # Keep track of old actor
-        self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])
-        # Keep track of old critic
-        self.OLDVPRED = OLDVPRED = tf.placeholder(tf.float32, [None])
-        self.LR = LR = tf.placeholder(tf.float32, [])
-        # Cliprange
-        self.CLIPRANGE = CLIPRANGE = tf.placeholder(tf.float32, [])
+                with tf.name_scope("neglogpac"):
+                    neglogpac = train_model.pd.neglogp(A)
 
-        neglogpac = train_model.pd.neglogp(A)
+                with tf.name_scope("entropy"):
+                    # Calculate the entropy
+                    # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
+                    entropy = tf.reduce_mean(train_model.pd.entropy())
+                    entropy_loss = (- ent_coef) * entropy
 
-        # Calculate the entropy
-        # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
-        entropy = tf.reduce_mean(train_model.pd.entropy())
+                with tf.name_scope("value_loss"):
+                    # CALCULATE THE LOSS
+                    value = train_model.value
+                    value_clipped = VALUE_PREV + tf.clip_by_value(value - VALUE_PREV, -CLIPRANGE, CLIPRANGE)
+                    vf_losses1 = tf.squared_difference(value, RETURNS)
+                    vf_losses2 = tf.squared_difference(value_clipped, RETURNS)
+                    vf_loss = 0.5 * vf_coef * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
 
-        # CALCULATE THE LOSS
-        # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
+                with tf.name_scope("policy_loss"):
+                    # Calculate ratio (pi current policy / pi old policy)
+                    ratio = tf.exp(OLDNEGLOGPAC - neglogpac)
+                    pg_losses = -ADV * ratio
+                    pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)
+                    pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))
 
-        # Clip the value to reduce variability during Critic training
-        # Get the predicted value
-        vpred = train_model.vf
-        vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE)
-        # Unclipped value
-        vf_losses1 = tf.square(vpred - R)
-        # Clipped value
-        vf_losses2 = tf.square(vpredclipped - R)
+                with tf.name_scope("approxkl"):
+                    approxkl = .5 * tf.reduce_mean(tf.squared_difference(neglogpac, OLDNEGLOGPAC))
 
-        vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
+                with tf.name_scope("clip_fraction"):
+                    clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
 
-        # Calculate ratio (pi current policy / pi old policy)
-        ratio = tf.exp(OLDNEGLOGPAC - neglogpac)
+                with tf.name_scope("total_loss"):
+                    loss = pg_loss + entropy_loss + vf_loss
 
-        # Defining Loss = - J is equivalent to max J
-        pg_losses = -ADV * ratio
+            with tf.variable_scope('optimizer'):
+                self.LR = LR = tf.placeholder(tf.float32, [], name='learning_rate')
 
-        pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)
+                # UPDATE THE PARAMETERS USING LOSS
+                # 1. Get the model parameters
+                params = tf.trainable_variables(self.scope.name)
 
-        # Final PG loss
-        pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))
-        approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))
-        clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
+                # 2. Build our trainer
+                if MPI is not None:
+                    self.trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+                else:
+                    self.trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
+                # 3. Calculate the gradients
+                grads_and_var = self.trainer.compute_gradients(loss, params)
+                grads, var = zip(*grads_and_var)
 
-        # Total loss
-        loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
+                if max_grad_norm is not None:
+                    # Clip the gradients (normalize)
+                    grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
+                grads_and_var = list(zip(grads, var))
 
-        # UPDATE THE PARAMETERS USING LOSS
-        # 1. Get the model parameters
-        params = tf.trainable_variables('ppo2_model')
-        # 2. Build our trainer
-        if comm is not None and comm.Get_size() > 1:
-            self.trainer = MpiAdamOptimizer(comm, learning_rate=LR, mpi_rank_weight=mpi_rank_weight, epsilon=1e-5)
-        else:
-            self.trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
-        # 3. Calculate the gradients
-        grads_and_var = self.trainer.compute_gradients(loss, params)
-        grads, var = zip(*grads_and_var)
+                self.grads = grads
+                self.var = var
+                self._train_op = self.trainer.apply_gradients(grads_and_var)
 
-        if max_grad_norm is not None:
-            # Clip the gradients (normalize)
-            grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
-        grads_and_var = list(zip(grads, var))
-        # zip aggregate each gradient with parameters associated
-        # For instance zip(ABCD, xyza) => Ax, By, Cz, Da
+                self.loss_names = ['policy_loss', 'value_loss', 'entropy_loss', 'approxkl', 'clipfrac',
+                                   'total_loss']
+                self.stats_list = [pg_loss, vf_loss, entropy_loss, approxkl, clipfrac, loss]
 
-        self.grads = grads
-        self.var = var
-        self._train_op = self.trainer.apply_gradients(grads_and_var)
-        self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']
-        self.stats_list = [pg_loss, vf_loss, entropy, approxkl, clipfrac]
+                self.train_model = train_model
+                self.act_model = act_model
+                self.initial_state = act_model.initial_state
 
+                self.save = functools.partial(save_variables, sess=sess)
+                self.load = functools.partial(load_variables, sess=sess)
 
-        self.train_model = train_model
-        self.act_model = act_model
-        self.step = act_model.step
-        self.value = act_model.value
-        self.initial_state = act_model.initial_state
+            with tf.variable_scope('initialization'):
+                sess.run(tf.initializers.variables(tf.global_variables(self.scope.name)))
+                sess.run(tf.initializers.variables(tf.local_variables(self.scope.name)))
+                global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope.name)
+                if MPI is not None:
+                    sync_from_root(sess, global_variables)  # pylint: disable=E1101
 
-        self.save = functools.partial(save_variables, sess=sess)
-        self.load = functools.partial(load_variables, sess=sess)
+    def step_with_dict(self, **kwargs):
+        return self.act_model.step(**kwargs)
 
-        initialize()
-        global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
-        if MPI is not None:
-            sync_from_root(sess, global_variables, comm=comm) #pylint: disable=E1101
-
-    def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):
-        # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
-        # Returns = R + yV(s')
-        advs = returns - values
+    def step(self, obs, M=None, S=None, **kwargs):
+        kwargs.update({'observations': obs})
+        if M is not None and S is not None:
+            kwargs.update({'dones': M})
+            kwargs.update({'states': S})
+        transition = self.act_model.step(**kwargs)
+        states = transition['next_states'] if 'next_states' in transition else None
+        return transition['actions'], transition['values'], states, transition['neglogpacs']
 
+    def train(self,
+              lr,
+              cliprange,
+              observations,
+              advs,
+              returns,
+              actions,
+              values,
+              neglogpacs,
+              **_kwargs):
         # Normalize the advantages
         advs = (advs - advs.mean()) / (advs.std() + 1e-8)
 
         td_map = {
-            self.train_model.X : obs,
-            self.A : actions,
-            self.ADV : advs,
-            self.R : returns,
-            self.LR : lr,
-            self.CLIPRANGE : cliprange,
-            self.OLDNEGLOGPAC : neglogpacs,
-            self.OLDVPRED : values
+            self.train_model.X: observations,
+            self.A: actions,
+            self.ADV: advs,
+            self.RETURNS: returns,
+            self.LR: lr,
+            self.CLIPRANGE: cliprange,
+            self.OLDNEGLOGPAC: neglogpacs,
+            self.VALUE_PREV: values,
         }
-        if states is not None:
-            td_map[self.train_model.S] = states
-            td_map[self.train_model.M] = masks
+
+        td_map.update(self.train_model.feed_dict(**_kwargs))
 
         return self.sess.run(
             self.stats_list + [self._train_op],
             td_map
         )[:-1]
-

baselines/ppo2/policies.py

@@ -0,0 +1,188 @@
+import gym
+import numpy as np
+import tensorflow as tf
+
+from baselines.a2c.utils import fc
+from baselines.common import tf_util
+from baselines.common.distributions import make_pdtype
+from baselines.common.input import observation_placeholder, encode_observation
+from baselines.common.models import get_network_builder
+from baselines.common.tf_util import adjust_shape
+from baselines.ppo2.layers import RNN
+
+
+class PolicyWithValue(object):
+    """
+    Encapsulates fields and methods for RL policy and two value function estimation with shared parameters
+    """
+
+    def __init__(self, env, observations, latent, dones, states=None, estimate_q=False, vf_latent=None, sess=None):
+        """
+        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.dones = dones
+        self.pdtype = make_pdtype(env.action_space)
+        self.states = states
+        self.sess = sess or tf.get_default_session()
+
+        vf_latent = vf_latent if vf_latent is not None else latent
+
+        with tf.variable_scope('policy'):
+            latent = tf.layers.flatten(latent)
+            # Based on the action space, will select what probability distribution type
+            self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01)
+
+            with tf.variable_scope('sample_action'):
+                self.action = self.pd.sample()
+
+            with tf.variable_scope('negative_log_probability'):
+                # Calculate the neg log of our probability
+                self.neglogp = self.pd.neglogp(self.action)
+
+        with tf.variable_scope('value'):
+            vf_latent = tf.layers.flatten(vf_latent)
+
+            if estimate_q:
+                assert isinstance(env.action_space, gym.spaces.Discrete)
+                self.q = fc(vf_latent, 'q', env.action_space.n)
+                self.value = self.q
+            else:
+                vf_latent = tf.layers.flatten(vf_latent)
+                self.value = fc(vf_latent, 'value', 1, init_scale=0.01)
+                self.value = self.value[:, 0]
+
+        self.step_input = {
+            'observations': observations,
+            'dones': self.dones,
+        }
+
+        self.step_output = {
+            'actions': self.action,
+            'values': self.value,
+            'neglogpacs': self.neglogp,
+        }
+        if self.states:
+            self.initial_state = np.zeros(self.states['current'].get_shape())
+            self.step_input.update({'states': self.states['current']})
+            self.step_output.update({'states': self.states['current'],
+                                     'next_states': self.states['next']})
+        else:
+            self.initial_state = None
+
+    def feed_dict(self, **kwargs):
+        feed_dict = {}
+        for key in kwargs:
+            if key in self.step_input:
+                feed_dict[self.step_input[key]] = adjust_shape(self.step_input[key], kwargs[key])
+        return feed_dict
+
+    def step(self, **kwargs):
+        return self.sess.run(self.step_output,
+                             feed_dict=self.feed_dict(**kwargs))
+
+    def values(self, **kwargs):
+        return self.sess.run({'values': self.value},
+                             feed_dict=self.feed_dict(**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_ppo_policy(env, policy_network, value_network=None, estimate_q=False, **policy_kwargs):
+    if isinstance(policy_network, str):
+        network_type = policy_network
+        policy_network = get_network_builder(network_type)(**policy_kwargs)
+
+    if value_network is None:
+        value_network = 'shared'
+
+    def policy_fn(nbatch=None, nsteps=None, sess=None, observ_placeholder=None):
+        next_states_list = []
+        state_map = {}
+        state_placeholder = None
+
+        ob_space = env.observation_space
+        X = observ_placeholder if observ_placeholder is not None else observation_placeholder(ob_space,
+                                                                                              batch_size=nbatch)
+        dones = tf.placeholder(tf.float32, shape=[X.shape[0]], name='dones')
+        encoded_x = encode_observation(ob_space, X)
+
+        with tf.variable_scope('current_rnn_memory'):
+            if value_network == 'shared':
+                value_network_ = value_network
+            else:
+                if value_network == 'copy':
+                    value_network_ = policy_network
+                else:
+                    assert callable(value_network)
+                    value_network_ = value_network
+
+            policy_memory_size = policy_network.memory_size if isinstance(policy_network, RNN) else 0
+            value_memory_size = value_network_.memory_size if isinstance(value_network_, RNN) else 0
+            state_size = policy_memory_size + value_memory_size
+
+            if state_size > 0:
+                state_placeholder = tf.placeholder(dtype=tf.float32, shape=(nbatch, state_size),
+                                                   name='states')
+
+                state_map['policy'] = state_placeholder[:, 0:policy_memory_size]
+                state_map['value'] = state_placeholder[:, policy_memory_size:]
+
+        with tf.variable_scope('policy_latent', reuse=tf.AUTO_REUSE):
+            if isinstance(policy_network, RNN):
+                assert policy_memory_size > 0
+                policy_latent, next_policy_state = \
+                    policy_network(encoded_x, dones, state_map['policy'])
+                next_states_list.append(next_policy_state)
+            else:
+                policy_latent = policy_network(encoded_x)
+
+        with tf.variable_scope('value_latent', reuse=tf.AUTO_REUSE):
+            if value_network_ == 'shared':
+                value_latent = policy_latent
+            elif isinstance(value_network_, RNN):
+                assert value_memory_size > 0
+                value_latent, next_value_state = \
+                    value_network_(encoded_x, dones, state_map['value'])
+                next_states_list.append(next_value_state)
+            else:
+                value_latent = value_network_(encoded_x)
+
+        with tf.name_scope("next_rnn_memory"):
+            if state_size > 0:
+                next_states = tf.concat(next_states_list, axis=1)
+                state_info = {'current': state_placeholder,
+                              'next': next_states, }
+            else:
+                state_info = None
+
+        policy = PolicyWithValue(
+            env=env,
+            observations=X,
+            dones=dones,
+            latent=policy_latent,
+            vf_latent=value_latent,
+            states=state_info,
+            sess=sess,
+            estimate_q=estimate_q,
+        )
+        return policy
+
+    return policy_fn

baselines/ppo2/ppo2.py

@@ -1,28 +1,35 @@
 import os
-import time
-import numpy as np
 import os.path as osp
-from baselines import logger
+import time
 from collections import deque
-from baselines.common import explained_variance, set_global_seeds
-from baselines.common.policies import build_policy
+
+import numpy as np
+import tensorflow as tf
+from baselines import logger
+from baselines.common import explained_variance
+from baselines.common import set_global_seeds
+from baselines.common.tf_util import display_var_info
+from baselines.ppo2.policies import build_ppo_policy
+from baselines.ppo2.runner import Runner
+
 try:
     from mpi4py import MPI
 except ImportError:
     MPI = None
-from baselines.ppo2.runner import Runner
 
 
 def constfn(val):
     def f(_):
         return val
+
     return f
 
-def learn(*, network, env, total_timesteps, eval_env = None, 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, model_fn=None, update_fn=None, init_fn=None, mpi_rank_weight=1, comm=None, **network_kwargs):
-    '''
+
+def learn(*, network, env, total_timesteps, eval_env=None, seed=None, nsteps=128, 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=10, load_path=None, model_fn=None, **network_kwargs):
+    """
     Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
 
     Parameters:
@@ -52,7 +59,7 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
 
     max_grad_norm: float or None      gradient norm clipping coefficient
 
-    gamma: float                      discounting factor
+    gamma: float                      discounting factor for rewards
 
     lam: float                        advantage estimation discounting factor (lambda in the paper)
 
@@ -72,20 +79,21 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
 
     **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)
-    if isinstance(cliprange, float): cliprange = constfn(cliprange)
-    else: assert callable(cliprange)
+    if isinstance(lr, float):
+        lr = constfn(lr)
+    else:
+        assert callable(lr)
+    if isinstance(cliprange, float):
+        cliprange = constfn(cliprange)
+    else:
+        assert callable(cliprange)
     total_timesteps = int(total_timesteps)
 
-    policy = build_policy(env, network, **network_kwargs)
+    policy = build_ppo_policy(env, network, **network_kwargs)
 
     # Get the nb of env
     nenvs = env.num_envs
@@ -97,7 +105,6 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
     # Calculate the batch_size
     nbatch = nenvs * nsteps
     nbatch_train = nbatch // nminibatches
-    is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0)
 
     # Instantiate the model object (that creates act_model and train_model)
     if model_fn is None:
@@ -105,28 +112,29 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
         model_fn = Model
 
     model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
-                    nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
-                    max_grad_norm=max_grad_norm, comm=comm, mpi_rank_weight=mpi_rank_weight)
+                     nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm)
 
     if load_path is not None:
         model.load(load_path)
+
+    allvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=model.name)
+    display_var_info(allvars)
+
     # Instantiate the runner object
-    runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
+    runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, ob_space=ob_space, lam=lam)
+
     if eval_env is not None:
-        eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
+        eval_runner = Runner(env=eval_env, model=model, nsteps=nsteps, gamma=gamma, ob_space=ob_space, lam=lam)
 
     epinfobuf = deque(maxlen=100)
     if eval_env is not None:
         eval_epinfobuf = deque(maxlen=100)
 
-    if init_fn is not None:
-        init_fn()
-
     # Start total timer
     tfirststart = time.perf_counter()
+    nupdates = total_timesteps // nbatch
 
-    nupdates = total_timesteps//nbatch
-    for update in range(1, nupdates+1):
+    for update in range(1, nupdates + 1):
         assert nbatch % nminibatches == 0
         # Start timer
         tstart = time.perf_counter()
@@ -136,48 +144,39 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
         # Calculate the cliprange
         cliprangenow = cliprange(frac)
 
-        if update % log_interval == 0 and is_mpi_root: logger.info('Stepping environment...')
-
         # Get minibatch
-        obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
+        minibatch = runner.run()
+
         if eval_env is not None:
-            eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
+            eval_minibatch = eval_runner.run()
+            _eval_obs = eval_minibatch['observations']  # noqa: F841
+            _eval_returns = eval_minibatch['returns']   # noqa: F841
+            _eval_masks = eval_minibatch['masks']       # noqa: F841
+            _eval_actions = eval_minibatch['actions']   # noqa: F841
+            _eval_values = eval_minibatch['values']     # noqa: F841
+            _eval_neglogpacs = eval_minibatch['neglogpacs'] # noqa: F841
+            _eval_states = eval_minibatch['state']      # noqa: F841
+            eval_epinfos = eval_minibatch['epinfos']
 
-        if update % log_interval == 0 and is_mpi_root: logger.info('Done.')
-
-        epinfobuf.extend(epinfos)
+        epinfobuf.extend(minibatch.pop('epinfos'))
         if eval_env is not None:
             eval_epinfobuf.extend(eval_epinfos)
 
         # Here what we're going to do is for each minibatch calculate the loss and append it.
         mblossvals = []
-        if states is None: # nonrecurrent version
-            # Index of each element of batch_size
-            # Create the indices array
-            inds = np.arange(nbatch)
-            for _ in range(noptepochs):
-                # Randomize the indexes
-                np.random.shuffle(inds)
-                # 0 to batch_size with batch_train_size step
-                for start in range(0, nbatch, nbatch_train):
-                    end = start + nbatch_train
-                    mbinds = inds[start:end]
-                    slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
-                    mblossvals.append(model.train(lrnow, cliprangenow, *slices))
-        else: # recurrent version
-            assert nenvs % nminibatches == 0
-            envsperbatch = nenvs // nminibatches
-            envinds = np.arange(nenvs)
-            flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
-            for _ in range(noptepochs):
-                np.random.shuffle(envinds)
-                for start in range(0, nenvs, envsperbatch):
-                    end = start + envsperbatch
-                    mbenvinds = envinds[start:end]
-                    mbflatinds = flatinds[mbenvinds].ravel()
-                    slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
-                    mbstates = states[mbenvinds]
-                    mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
+
+        # Index of each element of batch_size
+        # Create the indices array
+        inds = np.arange(nbatch)
+        for _ in range(noptepochs):
+            # Randomize the indexes
+            np.random.shuffle(inds)
+            # 0 to batch_size with batch_train_size step
+            for start in range(0, nbatch, nbatch_train):
+                end = start + nbatch_train
+                mbinds = inds[start:end]
+                slices = {key: minibatch[key][mbinds] for key in minibatch}
+                mblossvals.append(model.train(lrnow, cliprangenow, **slices))
 
         # Feedforward --> get losses --> update
         lossvals = np.mean(mblossvals, axis=0)
@@ -185,40 +184,39 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
         tnow = time.perf_counter()
         # Calculate the fps (frame per second)
         fps = int(nbatch / (tnow - tstart))
-
-        if update_fn is not None:
-            update_fn(update)
-
         if update % log_interval == 0 or update == 1:
             # Calculates if value function is a good predicator of the returns (ev > 1)
             # or if it's just worse than predicting nothing (ev =< 0)
-            ev = explained_variance(values, returns)
-            logger.logkv("misc/serial_timesteps", update*nsteps)
-            logger.logkv("misc/nupdates", update)
-            logger.logkv("misc/total_timesteps", update*nbatch)
+            ev = explained_variance(minibatch['values'], minibatch['returns'])
+            logger.logkv("serial_timesteps", update * nsteps)
+            logger.logkv("nupdates", update)
+            logger.logkv("total_timesteps", update * nbatch)
             logger.logkv("fps", fps)
-            logger.logkv("misc/explained_variance", float(ev))
+            logger.logkv("explained_variance", float(ev))
             logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
             logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
-            if eval_env is not None:
-                logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
-                logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
-            logger.logkv('misc/time_elapsed', tnow - tfirststart)
-            for (lossval, lossname) in zip(lossvals, model.loss_names):
-                logger.logkv('loss/' + lossname, lossval)
+            logger.logkv('rewards_per_step', safemean(minibatch['rewards']))
+            logger.logkv('advantages_per_step', safemean(minibatch['advs']))
 
-            logger.dumpkvs()
-        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and is_mpi_root:
+            if eval_env is not None:
+                logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]))
+                logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]))
+            logger.logkv('time_elapsed', tnow - tfirststart)
+            for (lossval, lossname) in zip(lossvals, model.loss_names):
+                logger.logkv(lossname, lossval)
+            if MPI is None or 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 is None or 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)
+            savepath = osp.join(checkdir, '%.5i' % update)
             print('Saving to', savepath)
             model.save(savepath)
-
+        del minibatch
     return model
+
+
 # Avoid division error when calculate the mean (in our case if epinfo is empty returns np.nan, not return an error)
 def safemean(xs):
     return np.nan if len(xs) == 0 else np.mean(xs)
-
-
-

baselines/ppo2/runner.py

@@ -1,6 +1,8 @@
 import numpy as np
+
 from baselines.common.runners import AbstractEnvRunner
 
+
 class Runner(AbstractEnvRunner):
     """
     We use this object to make a mini batch of experiences
@@ -10,67 +12,118 @@ class Runner(AbstractEnvRunner):
     run():
     - Make a mini batch
     """
-    def __init__(self, *, env, model, nsteps, gamma, lam):
+
+    def __init__(self, *, env, model, nsteps, gamma, ob_space, lam):
         super().__init__(env=env, model=model, nsteps=nsteps)
-        # Lambda used in GAE (General Advantage Estimation)
-        self.lam = lam
-        # Discount rate
-        self.gamma = gamma
+
+        self.lam = lam  # Lambda used in GAE (General Advantage Estimation)
+        self.gamma = gamma  # Discount rate for rewards
+        self.ob_space = ob_space
 
     def run(self):
-        # Here, we init the lists that will contain the mb of experiences
-        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]
-        mb_states = self.states
+        minibatch = {
+            "observations": [],
+            "actions": [],
+            "rewards": [],
+            "values": [],
+            "dones": [],
+            "neglogpacs": [],
+        }
+
+        data_type = {
+            "observations": self.obs.dtype,
+            "actions": np.float32,
+            "rewards": np.float32,
+            "values": np.float32,
+            "dones": np.float32,
+            "neglogpacs": np.float32,
+        }
+
+        prev_transition = {'next_states': self.model.initial_state} if self.model.initial_state is not None else {}
         epinfos = []
+
         # For n in range number of steps
         for _ in range(self.nsteps):
-            # Given observations, get action value and neglopacs
-            # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
-            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)
-            mb_neglogpacs.append(neglogpacs)
-            mb_dones.append(self.dones)
+            transitions = {}
+            transitions['observations'] = self.obs.copy()
+            transitions['dones'] = self.dones
+            if 'next_states' in prev_transition:
+                transitions['states'] = prev_transition['next_states']
+            transitions.update(self.model.step_with_dict(**transitions))
 
             # Take actions in env and look the results
             # Infos contains a ton of useful informations
-            self.obs[:], rewards, self.dones, infos = self.env.step(actions)
+            self.obs, transitions['rewards'], self.dones, infos = self.env.step(transitions['actions'])
+            self.dones = np.array(self.dones, dtype=np.float)
+
             for info in infos:
                 maybeepinfo = info.get('episode')
-                if maybeepinfo: epinfos.append(maybeepinfo)
-            mb_rewards.append(rewards)
-        #batch of steps to batch of rollouts
-        mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)
-        mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
-        mb_actions = np.asarray(mb_actions)
-        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, S=self.states, M=self.dones)
+                if maybeepinfo:
+                    epinfos.append(maybeepinfo)
 
-        # discount/bootstrap off value fn
-        mb_returns = np.zeros_like(mb_rewards)
-        mb_advs = np.zeros_like(mb_rewards)
-        lastgaelam = 0
+            for key in transitions:
+                if key not in minibatch:
+                    minibatch[key] = []
+                minibatch[key].append(transitions[key])
+            prev_transition = transitions
+
+        for key in minibatch:
+            dtype = data_type[key] if key in data_type else np.float
+            minibatch[key] = np.array(minibatch[key], dtype=dtype)
+
+        transitions['observations'] = self.obs.copy()
+        transitions['dones'] = self.dones
+        if 'states' in transitions:
+            transitions['states'] = transitions.pop('next_states')
+
+        for key in minibatch:
+            dtype = data_type[key] if key in data_type else np.float
+            minibatch[key] = np.asarray(minibatch[key], dtype=dtype)
+
+        last_values = self.model.step_with_dict(**transitions)['values']
+
+        # Calculate returns and advantages.
+        minibatch['advs'], minibatch['returns'] = \
+            self.advantage_and_returns(values=minibatch['values'],
+                                       rewards=minibatch['rewards'],
+                                       dones=minibatch['dones'],
+                                       last_values=last_values,
+                                       last_dones=self.dones,
+                                       gamma=self.gamma)
+
+        for key in minibatch:
+            minibatch[key] = sf01(minibatch[key])
+
+        minibatch['epinfos'] = epinfos
+        return minibatch
+
+    def advantage_and_returns(self, values, rewards, dones, last_values, last_dones, gamma,
+                              use_non_episodic_rewards=False):
+        """
+        calculate Generalized Advantage Estimation (GAE), https://arxiv.org/abs/1506.02438
+        see also Proximal Policy Optimization Algorithms, https://arxiv.org/abs/1707.06347
+        """
+
+        advantages = np.zeros_like(rewards)
+        lastgaelam = 0  # Lambda used in General Advantage Estimation
         for t in reversed(range(self.nsteps)):
-            if t == self.nsteps - 1:
-                nextnonterminal = 1.0 - self.dones
-                nextvalues = last_values
+            if not use_non_episodic_rewards:
+                if t == self.nsteps - 1:
+                    next_non_terminal = 1.0 - last_dones
+                else:
+                    next_non_terminal = 1.0 - dones[t + 1]
             else:
-                nextnonterminal = 1.0 - mb_dones[t+1]
-                nextvalues = mb_values[t+1]
-            delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]
-            mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam
-        mb_returns = mb_advs + mb_values
-        return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),
-            mb_states, epinfos)
-# obs, returns, masks, actions, values, neglogpacs, states = runner.run()
+                next_non_terminal = 1.0
+            next_value = values[t + 1] if t < self.nsteps - 1 else last_values
+            delta = rewards[t] + gamma * next_value * next_non_terminal - values[t]
+            advantages[t] = lastgaelam = delta + gamma * self.lam * next_non_terminal * lastgaelam
+        returns = advantages + values
+        return advantages, returns
+
+
 def sf01(arr):
     """
     swap and then flatten axes 0 and 1
     """
     s = arr.shape
     return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
-
-

baselines/run.py

@@ -113,7 +113,7 @@ def build_env(args):
         env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations)
 
         if env_type == 'mujoco':
-            env = VecNormalize(env, use_tf=True)
+            env = VecNormalize(env)
 
     return env
 

setup.py

@@ -12,9 +12,10 @@ extras = {
         'filelock',
         'pytest',
         'pytest-forked',
-        'atari-py',
-        'matplotlib',
-        'pandas'
+        'atari-py'
+    ],
+    'bullet': [
+        'pybullet',
     ],
     'mpi': [
         'mpi4py'
@@ -31,10 +32,12 @@ setup(name='baselines',
       packages=[package for package in find_packages()
                 if package.startswith('baselines')],
       install_requires=[
-          'gym>=0.10.0, <1.0.0',
+          'gym',
           'scipy',
           'tqdm',
           'joblib',
+          'dill',
+          'progressbar2',
           'cloudpickle',
           'click',
           'opencv-python'