Update cmd_util with initializer, env_kwargs, and force_dummy

.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 --forked .

README.md

@@ -89,7 +89,7 @@ python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timeste
 will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same)
 
 See docstrings in [common/models.py](baselines/common/models.py) for description of network parameters for each type of model, and 
-docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparameters. 
+docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparamters. 
 
 ### Example 2. DQN on Atari 
 DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong:
@@ -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/a2c/a2c.py

@@ -11,8 +11,6 @@ from baselines.common.policies import build_policy
 
 from baselines.a2c.utils import Scheduler, find_trainable_variables
 from baselines.a2c.runner import Runner
-from baselines.ppo2.ppo2 import safemean
-from collections import deque
 
 from tensorflow import losses
 
@@ -197,7 +195,6 @@ def learn(
 
     # Instantiate the runner object
     runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
-    epinfobuf = deque(maxlen=100)
 
     # Calculate the batch_size
     nbatch = nenvs*nsteps
@@ -207,8 +204,7 @@ def learn(
 
     for update in range(1, total_timesteps//nbatch+1):
         # Get mini batch of experiences
-        obs, states, rewards, masks, actions, values, epinfos = runner.run()
-        epinfobuf.extend(epinfos)
+        obs, states, rewards, masks, actions, values = runner.run()
 
         policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
         nseconds = time.time()-tstart
@@ -225,8 +221,6 @@ def learn(
             logger.record_tabular("policy_entropy", float(policy_entropy))
             logger.record_tabular("value_loss", float(value_loss))
             logger.record_tabular("explained_variance", float(ev))
-            logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
-            logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
             logger.dump_tabular()
     return model
 

baselines/a2c/runner.py

@@ -22,7 +22,6 @@ class Runner(AbstractEnvRunner):
         # We initialize the lists that will contain the mb of experiences
         mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[]
         mb_states = self.states
-        epinfos = []
         for n in range(self.nsteps):
             # Given observations, take action and value (V(s))
             # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
@@ -35,10 +34,7 @@ class Runner(AbstractEnvRunner):
             mb_dones.append(self.dones)
 
             # Take actions in env and look the results
-            obs, rewards, dones, infos = self.env.step(actions)
-            for info in infos:
-                maybeepinfo = info.get('episode')
-                if maybeepinfo: epinfos.append(maybeepinfo)
+            obs, rewards, dones, _ = self.env.step(actions)
             self.states = states
             self.dones = dones
             self.obs = obs
@@ -73,4 +69,4 @@ class Runner(AbstractEnvRunner):
         mb_rewards = mb_rewards.flatten()
         mb_values = mb_values.flatten()
         mb_masks = mb_masks.flatten()
-        return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values, epinfos
+        return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values

baselines/acktr/acktr.py

@@ -11,8 +11,6 @@ from baselines.common.tf_util import get_session, save_variables, load_variables
 from baselines.a2c.runner import Runner
 from baselines.a2c.utils import Scheduler, find_trainable_variables
 from baselines.acktr import kfac
-from baselines.ppo2.ppo2 import safemean
-from collections import deque
 
 
 class Model(object):
@@ -92,7 +90,7 @@ 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)
@@ -120,7 +118,6 @@ def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interva
         model.load(load_path)
 
     runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
-    epinfobuf = deque(maxlen=100)
     nbatch = nenvs*nsteps
     tstart = time.time()
     coord = tf.train.Coordinator()
@@ -130,8 +127,7 @@ def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interva
         enqueue_threads = []
 
     for update in range(1, total_timesteps//nbatch+1):
-        obs, states, rewards, masks, actions, values, epinfos = runner.run()
-        epinfobuf.extend(epinfos)
+        obs, states, rewards, masks, actions, values = runner.run()
         policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
         model.old_obs = obs
         nseconds = time.time()-tstart
@@ -145,8 +141,6 @@ def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interva
             logger.record_tabular("policy_loss", float(policy_loss))
             logger.record_tabular("value_loss", float(value_loss))
             logger.record_tabular("explained_variance", float(ev))
-            logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
-            logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
             logger.dump_tabular()
 
         if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():

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/bench/benchmarks.py

@@ -20,7 +20,7 @@ def register_benchmark(benchmark):
     if 'tasks' in benchmark:
         for t in benchmark['tasks']:
             if 'desc' not in t:
-                t['desc'] = remove_version_re.sub('', t.get('env_id', t.get('id')))
+                t['desc'] = remove_version_re.sub('', t['env_id'])
     _BENCHMARKS.append(benchmark)
 
 

baselines/bench/monitor.py

@@ -16,13 +16,11 @@ class Monitor(Wrapper):
     def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
         Wrapper.__init__(self, env=env)
         self.tstart = time.time()
-        if filename:
-            self.results_writer = ResultsWriter(filename,
-                header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
-                extra_keys=reset_keywords + info_keywords
-            )
-        else:
-            self.results_writer = None
+        self.results_writer = ResultsWriter(
+            filename,
+            header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
+            extra_keys=reset_keywords + info_keywords
+        )
         self.reset_keywords = reset_keywords
         self.info_keywords = info_keywords
         self.allow_early_resets = allow_early_resets
@@ -70,9 +68,8 @@ class Monitor(Wrapper):
             self.episode_lengths.append(eplen)
             self.episode_times.append(time.time() - self.tstart)
             epinfo.update(self.current_reset_info)
-            if self.results_writer:
-                self.results_writer.write_row(epinfo)
-            assert isinstance(info, dict)
+            self.results_writer.write_row(epinfo)
+
             if isinstance(info, dict):
                 info['episode'] = epinfo
 
@@ -99,21 +96,24 @@ class LoadMonitorResultsError(Exception):
 
 
 class ResultsWriter(object):
-    def __init__(self, filename, header='', extra_keys=()):
+    def __init__(self, filename=None, header='', extra_keys=()):
         self.extra_keys = extra_keys
-        assert filename is not None
-        if not filename.endswith(Monitor.EXT):
-            if osp.isdir(filename):
-                filename = osp.join(filename, Monitor.EXT)
-            else:
-                filename = filename + "." + Monitor.EXT
-        self.f = open(filename, "wt")
-        if isinstance(header, dict):
-            header = '# {} \n'.format(json.dumps(header))
-        self.f.write(header)
-        self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
-        self.logger.writeheader()
-        self.f.flush()
+        if filename is None:
+            self.f = None
+            self.logger = None
+        else:
+            if not filename.endswith(Monitor.EXT):
+                if osp.isdir(filename):
+                    filename = osp.join(filename, Monitor.EXT)
+                else:
+                    filename = filename + "." + Monitor.EXT
+            self.f = open(filename, "wt")
+            if isinstance(header, dict):
+                header = '# {} \n'.format(json.dumps(header))
+            self.f.write(header)
+            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
+            self.logger.writeheader()
+            self.f.flush()
 
     def write_row(self, epinfo):
         if self.logger:
@@ -121,6 +121,7 @@ class ResultsWriter(object):
             self.f.flush()
 
 
+
 def get_monitor_files(dir):
     return glob(osp.join(dir, "*" + Monitor.EXT))
 

baselines/common/atari_wrappers.py

@@ -6,8 +6,6 @@ import gym
 from gym import spaces
 import cv2
 cv2.ocl.setUseOpenCL(False)
-from .wrappers import TimeLimit
-
 
 class NoopResetEnv(gym.Wrapper):
     def __init__(self, env, noop_max=30):
@@ -130,60 +128,27 @@ 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):
@@ -254,15 +219,16 @@ class LazyFrames(object):
         return len(self._force())
 
     def __getitem__(self, i):
-        return self._force()[..., i]
+        return self._force()[i]
 
-def make_atari(env_id, max_episode_steps=None):
+def make_atari(env_id, timelimit=True):
+    # XXX(john): remove timelimit argument after gym is upgraded to allow double wrapping
     env = gym.make(env_id)
+    if not timelimit:
+        env = env.env
     assert 'NoFrameskip' in env.spec.id
     env = NoopResetEnv(env, noop_max=30)
     env = MaxAndSkipEnv(env, skip=4)
-    if max_episode_steps is not None:
-        env = TimeLimit(env, max_episode_steps=max_episode_steps)
     return env
 
 def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):

baselines/common/cmd_util.py

@@ -17,22 +17,20 @@ 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,
+                 env_kwargs=None,
                  force_dummy=False):
     """
     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()
@@ -47,9 +45,9 @@ 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
+            initializer=initializer,
+            env_kwargs=env_kwargs,
         )
 
     set_global_seeds(seed)
@@ -59,18 +57,11 @@ def make_vec_env(env_id, env_type, num_env, seed,
         return DummyVecEnv([make_thunk(i + start_index, initializer=None) for i in range(num_env)])
 
 
-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):
+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, initializer=None, env_kwargs=None):
     if initializer is not None:
         initializer(mpi_rank=mpi_rank, subrank=subrank)
 
     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 +69,7 @@ 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, **(env_kwargs or {}))
 
     if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
         keys = env.observation_space.spaces.keys()
@@ -89,17 +80,11 @@ 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':
-        if 'frame_stack' not in wrapper_kwargs:
-            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)
 
@@ -171,6 +156,7 @@ def common_arg_parser():
     parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int)
     parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int)
     parser.add_argument('--play', default=False, action='store_true')
+    parser.add_argument('--extra_import', help='Extra module to import to access external environments', type=str, default=None)
     return parser
 
 def robotics_arg_parser():

baselines/common/distributions.py

@@ -206,8 +206,7 @@ class CategoricalPd(Pd):
 class MultiCategoricalPd(Pd):
     def __init__(self, nvec, flat):
         self.flat = flat
-        self.categoricals = list(map(CategoricalPd,
-            tf.split(flat, np.array(nvec, dtype=np.int32), axis=-1)))
+        self.categoricals = list(map(CategoricalPd, tf.split(flat, nvec, axis=-1)))
     def flatparam(self):
         return self.flat
     def mode(self):
@@ -275,133 +274,6 @@ 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 +285,9 @@ 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/misc_util.py

@@ -13,6 +13,27 @@ def zipsame(*seqs):
     return zip(*seqs)
 
 
+def unpack(seq, sizes):
+    """
+    Unpack 'seq' into a sequence of lists, with lengths specified by 'sizes'.
+    None = just one bare element, not a list
+
+    Example:
+    unpack([1,2,3,4,5,6], [3,None,2]) -> ([1,2,3], 4, [5,6])
+    """
+    seq = list(seq)
+    it = iter(seq)
+    assert sum(1 if s is None else s for s in sizes) == len(seq), "Trying to unpack %s into %s" % (seq, sizes)
+    for size in sizes:
+        if size is None:
+            yield it.__next__()
+        else:
+            li = []
+            for _ in range(size):
+                li.append(it.__next__())
+            yield li
+
+
 class EzPickle(object):
     """Objects that are pickled and unpickled via their constructor
     arguments.

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

@@ -1,90 +1,31 @@
 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:
-    MPI = None
+from mpi4py import MPI
 
 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))
+
+        def _collect_grads(flat_grad):
             self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
-            np.divide(buf, float(total_weight), out=buf)
-            if countholder[0] % 100 == 0:
-                check_synced(np_stat, self.comm)
-            countholder[0] += 1
+            np.divide(buf, float(num_tasks), out=buf)
             return buf
 
-        avg_flat_grad = tf.py_func(_collect_grads, [flat_grad, stat], tf.float32)
+        avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
         avg_flat_grad.set_shape(flat_grad.shape)
         avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
         avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
                     for g, (_, v) in zip(avg_grads, grads_and_vars)]
+
         return avg_grads_and_vars
-
-def check_synced(localval, comm=None):
-    """
-    It's common to forget to initialize your variables to the same values, or
-    (less commonly) if you update them in some other way than adam, to get them out of sync.
-    This function checks that variables on all MPI workers are the same, and raises
-    an AssertionError otherwise
-
-    Arguments:
-        comm: MPI communicator
-        localval: list of local variables (list of variables on current worker to be compared with the other workers)
-    """
-    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}'
-
-@with_mpi(timeout=5)
-def test_nonfreeze():
-    np.random.seed(0)
-    tf.set_random_seed(0)
-
-    a = tf.Variable(np.random.randn(3).astype('float32'))
-    b = tf.Variable(np.random.randn(2,5).astype('float32'))
-    loss = tf.reduce_sum(tf.square(a)) + tf.reduce_sum(tf.sin(b))
-
-    stepsize = 1e-2
-    # for some reason the session config with inter_op_parallelism_threads was causing
-    # nested sess.run calls to freeze
-    config = tf.ConfigProto(inter_op_parallelism_threads=1)
-    sess = U.get_session(config=config)
-    update_op = MpiAdamOptimizer(comm=MPI.COMM_WORLD, learning_rate=stepsize).minimize(loss)
-    sess.run(tf.global_variables_initializer())
-    losslist_ref = []
-    for i in range(100):
-        l,_ = sess.run([loss, update_op])
-        print(i, l)
-        losslist_ref.append(l)

baselines/common/mpi_util.py

@@ -1,16 +1,9 @@
 from collections import defaultdict
+from mpi4py import MPI
 import os, numpy as np
 import platform
 import shutil
 import subprocess
-import warnings
-import sys
-
-try:
-    from mpi4py import MPI
-except ImportError:
-    MPI = None
-
 
 def sync_from_root(sess, variables, comm=None):
     """
@@ -20,10 +13,15 @@ def sync_from_root(sess, variables, comm=None):
       variables: all parameter variables including optimizer's
     """
     if comm is None: comm = MPI.COMM_WORLD
-    import tensorflow as tf
-    values = comm.bcast(sess.run(variables))
-    sess.run([tf.assign(var, val)
-        for (var, val) in zip(variables, values)])
+    rank = comm.Get_rank()
+    for var in variables:
+        if rank == 0:
+            comm.Bcast(sess.run(var))
+        else:
+            import tensorflow as tf
+            returned_var = np.empty(var.shape, dtype='float32')
+            comm.Bcast(returned_var)
+            sess.run(tf.assign(var, returned_var))
 
 def gpu_count():
     """
@@ -36,15 +34,13 @@ def gpu_count():
 
 def setup_mpi_gpus():
     """
-    Set CUDA_VISIBLE_DEVICES to MPI rank if not already set
+    Set CUDA_VISIBLE_DEVICES using MPI.
     """
-    if 'CUDA_VISIBLE_DEVICES' not in os.environ:
-        if sys.platform == 'darwin': # This Assumes if you're on OSX you're just
-            ids = []                 # doing a smoke test and don't want GPUs
-        else:
-            lrank, _lsize = get_local_rank_size(MPI.COMM_WORLD)
-            ids = [lrank]
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, ids))
+    num_gpus = gpu_count()
+    if num_gpus == 0:
+        return
+    local_rank, _ = get_local_rank_size(MPI.COMM_WORLD)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(local_rank % num_gpus)
 
 def get_local_rank_size(comm):
     """
@@ -85,9 +81,6 @@ def share_file(comm, path):
     comm.Barrier()
 
 def dict_gather(comm, d, op='mean', assert_all_have_data=True):
-    """
-    Perform a reduction operation over dicts
-    """
     if comm is None: return d
     alldicts = comm.allgather(d)
     size = comm.size
@@ -106,28 +99,3 @@ def dict_gather(comm, d, op='mean', assert_all_have_data=True):
         else:
             assert 0, op
     return result
-
-def mpi_weighted_mean(comm, local_name2valcount):
-    """
-    Perform a weighted average over dicts that are each on a different node
-    Input: local_name2valcount: dict mapping key -> (value, count)
-    Returns: key -> mean
-    """
-    all_name2valcount = comm.gather(local_name2valcount)
-    if comm.rank == 0:
-        name2sum = defaultdict(float)
-        name2count = defaultdict(float)
-        for n2vc in all_name2valcount:
-            for (name, (val, count)) in n2vc.items():
-                try:
-                    val = float(val)
-                except ValueError:
-                    if comm.rank == 0:
-                        warnings.warn('WARNING: tried to compute mean on non-float {}={}'.format(name, val))
-                else:
-                    name2sum[name] += val * count
-                    name2count[name] += count
-        return {name : name2sum[name] / name2count[name] for name in name2sum}
-    else:
-        return {}
-

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
 
@@ -249,9 +249,6 @@ def plot_results(
     legend_outside=False,
     resample=0,
     smooth_step=1.0,
-    tiling='vertical',
-    xlabel=None,
-    ylabel=None
 ):
     '''
     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/retro_wrappers.py

@@ -1,11 +1,25 @@
-from collections import deque
-import cv2
-cv2.ocl.setUseOpenCL(False)
-from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
-from .wrappers import TimeLimit
+ # flake8: noqa F403, F405
+from .atari_wrappers import *
 import numpy as np
 import gym
 
+class TimeLimit(gym.Wrapper):
+    def __init__(self, env, max_episode_steps=None):
+        super(TimeLimit, self).__init__(env)
+        self._max_episode_steps = max_episode_steps
+        self._elapsed_steps = 0
+
+    def step(self, ac):
+        observation, reward, done, info = self.env.step(ac)
+        self._elapsed_steps += 1
+        if self._elapsed_steps >= self._max_episode_steps:
+            done = True
+            info['TimeLimit.truncated'] = True
+        return observation, reward, done, info
+
+    def reset(self, **kwargs):
+        self._elapsed_steps = 0
+        return self.env.reset(**kwargs)
 
 class StochasticFrameSkip(gym.Wrapper):
     def __init__(self, env, n, stickprob):
@@ -85,7 +99,7 @@ class Downsample(gym.ObservationWrapper):
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, oldc) = env.observation_space.shape
         newshape = (oldh//ratio, oldw//ratio, oldc)
-        self.observation_space = gym.spaces.Box(low=0, high=255,
+        self.observation_space = spaces.Box(low=0, high=255,
             shape=newshape, dtype=np.uint8)
 
     def observation(self, frame):
@@ -102,7 +116,7 @@ class Rgb2gray(gym.ObservationWrapper):
         """
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, _oldc) = env.observation_space.shape
-        self.observation_space = gym.spaces.Box(low=0, high=255,
+        self.observation_space = spaces.Box(low=0, high=255,
             shape=(oldh, oldw, 1), dtype=np.uint8)
 
     def observation(self, frame):
@@ -199,10 +213,8 @@ class StartDoingRandomActionsWrapper(gym.Wrapper):
                 self.some_random_steps()
         return self.last_obs, rew, done, info
 
-def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs):
+def make_retro(*, game, state, max_episode_steps, **kwargs):
     import retro
-    if state is None:
-        state = retro.State.DEFAULT
     env = retro.make(game, state, **kwargs)
     env = StochasticFrameSkip(env, n=4, stickprob=0.25)
     if max_episode_steps is not None:
@@ -215,8 +227,7 @@ def wrap_deepmind_retro(env, scale=True, frame_stack=4):
     """
     env = WarpFrame(env)
     env = ClipRewardEnv(env)
-    if frame_stack > 1:
-        env = FrameStack(env, frame_stack)
+    env = FrameStack(env, frame_stack)
     if scale:
         env = ScaledFloatFrame(env)
     return env

baselines/common/running_mean_std.py

@@ -177,7 +177,7 @@ def profile_tf_runningmeanstd():
     outfile = '/tmp/timeline.json'
     with open(outfile, 'wt') as f:
         f.write(chrome_trace)
-    print('Successfully saved profile to {}. Exiting.'.format(outfile))
+    print(f'Successfully saved profile to {outfile}. Exiting.')
     exit(0)
     '''
 

baselines/common/test_mpi_util.py

@@ -1,29 +0,0 @@
-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
-
-@with_mpi()
-def test_mpi_weighted_mean():
-    comm = MPI.COMM_WORLD
-    with logger.scoped_configure(comm=comm):
-        if comm.rank == 0:
-            name2valcount = {'a' : (10, 2), 'b' : (20,3)}
-        elif comm.rank == 1:
-            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:
-            assert d == correctval, '{} != {}'.format(d, correctval)
-
-        for name, (val, count) in name2valcount.items():
-            for _ in range(count):
-                logger.logkv_mean(name, val)
-        d2 = logger.dumpkvs()
-        if comm.rank == 0:
-            assert d2 == correctval

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

@@ -7,16 +7,19 @@ class FixedSequenceEnv(Env):
     def __init__(
             self,
             n_actions=10,
+            seed=0,
             episode_len=100
     ):
+        self.np_random = np.random.RandomState()
+        self.np_random.seed(seed)
+        self.sequence = [self.np_random.randint(0, n_actions-1) for _ in range(episode_len)]
+
         self.action_space = Discrete(n_actions)
         self.observation_space = Discrete(1)
-        self.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
 
+        self.episode_len = episode_len
+        self.time = 0
+        self.reset()
 
     def reset(self):
         self.time = 0
@@ -27,13 +30,11 @@ 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, {}
 
-    def seed(self, seed=None):
-        self.np_random.seed(seed)
-
     def _choose_next_state(self):
         self.time += 1
 

baselines/common/tests/envs/identity_env.py

@@ -2,45 +2,41 @@ 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
+        self.observation_space = self.action_space
 
-        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 = self._get_reward(actions)
+        self._choose_next_state()
+        done = False
+        if self.episode_len and self.time >= self.episode_len:
             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, {}
+            done = True
 
-    def seed(self, seed=None):
-        self.action_space.seed(seed)
+        return self.state, rew, done, {}
+
+    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 +45,26 @@ 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):
@@ -81,10 +74,10 @@ class BoxIdentityEnv(IdentityEnv):
             episode_len=None,
     ):
 
-        self.action_space = Box(low=-1.0, high=1.0, shape=shape, dtype=np.float32)
+        self.action_space = Box(low=-1.0, high=1.0, shape=shape)
         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/envs/mnist_env.py

@@ -9,6 +9,7 @@ from gym.spaces import Discrete, Box
 class MnistEnv(Env):
     def __init__(
             self,
+            seed=0,
             episode_len=None,
             no_images=None
     ):
@@ -22,6 +23,7 @@ class MnistEnv(Env):
            self.mnist = input_data.read_data_sets(mnist_path)
 
         self.np_random = np.random.RandomState()
+        self.np_random.seed(seed)
 
         self.observation_space = Box(low=0.0, high=1.0, shape=(28,28,1))
         self.action_space = Discrete(10)
@@ -48,9 +50,6 @@ class MnistEnv(Env):
 
         return self.state[0], rew, done, {}
 
-    def seed(self, seed=None):
-        self.np_random.seed(seed)
-
     def train_mode(self):
         self.dataset = self.mnist.train
 

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,
@@ -23,7 +21,7 @@ learn_kwargs = {
 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):
@@ -35,7 +33,8 @@ def test_fixed_sequence(alg, rnn):
     kwargs = learn_kwargs[alg]
     kwargs.update(common_kwargs)
 
-    env_fn = lambda: FixedSequenceEnv(n_actions=10, episode_len=5)
+    episode_len = 5
+    env_fn = lambda: FixedSequenceEnv(10, episode_len=episode_len)
     learn = lambda e: get_learn_function(alg)(
         env=e,
         network=rnn,

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):
     '''
@@ -41,7 +41,7 @@ def test_mnist(alg):
 
     learn = get_learn_function(alg)
     learn_fn = lambda e: learn(env=e, **learn_kwargs)
-    env_fn = lambda: MnistEnv(episode_len=100)
+    env_fn = lambda: MnistEnv(seed=0, episode_len=100)
 
     simple_test(env_fn, learn_fn, 0.6)
 

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

@@ -44,12 +44,7 @@ def test_serialization(learn_fn, network_fn):
             # github issue: https://github.com/openai/baselines/issues/660
             return
 
-    def make_env():
-        env = MnistEnv(episode_len=100)
-        env.seed(10)
-        return env
-
-    env = DummyVecEnv([make_env])
+    env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)])
     ob = env.reset().copy()
     learn = get_learn_function(learn_fn)
 

baselines/common/tests/test_with_mpi.py

@@ -1,38 +0,0 @@
-import os
-import sys
-import subprocess
-import cloudpickle
-import base64
-import pytest
-from functools import wraps
-
-try:
-    from mpi4py import MPI
-except ImportError:
-    MPI = None
-
-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([
-                'mpiexec','-n', str(nproc),
-                sys.executable,
-                '-m', 'baselines.common.tests.test_with_mpi',
-                serialized_fn
-            ], env=os.environ, timeout=timeout)
-
-        if skip_if_no_mpi:
-            return pytest.mark.skipif(MPI is None, reason="MPI not present")(thunk)
-        else:
-            return thunk
-
-    return outer_thunk
-
-
-if __name__ == '__main__':
-    if len(sys.argv) > 1:
-        fn = cloudpickle.loads(base64.b64decode(sys.argv[1]))
-        assert callable(fn)
-        fn()

baselines/common/tests/util.py

@@ -1,50 +1,56 @@
 import tensorflow as tf
 import numpy as np
+from gym.spaces import np_random
 from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 
 N_TRIALS = 10000
 N_EPISODES = 100
 
-_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()
-        env.seed(0)
-        return env
-
     np.random.seed(0)
-    env = DummyVecEnv([seeded_env_fn])
-    with tf.Graph().as_default(), tf.Session(config=_sess_config).as_default():
+    np_random.seed(0)
+
+    env = DummyVecEnv([env_fn])
+
+
+    with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
         tf.set_random_seed(0)
+
         model = learn_fn(env)
+
         sum_rew = 0
         done = True
+
         for i in range(n_trials):
             if done:
                 obs = env.reset()
                 state = model.initial_state
+
             if state is not None:
                 a, v, state, _ = model.step(obs, S=state, M=[False])
             else:
                 a, v, _, _ = model.step(obs)
+
             obs, rew, done, _ = env.step(a)
             sum_rew += float(rew)
+
         print("Reward in {} trials is {}".format(n_trials, sum_rew))
         assert sum_rew > min_reward_fraction * n_trials, \
             'sum of rewards {} is less than {} of the total number of trials {}'.format(sum_rew, min_reward_fraction, n_trials)
 
+
+
 def reward_per_episode_test(env_fn, learn_fn, min_avg_reward, n_trials=N_EPISODES):
     env = DummyVecEnv([env_fn])
-    with tf.Graph().as_default(), tf.Session(config=_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)
         rewards = [sum(r) for r in rewards]
+
         avg_rew = sum(rewards) / N_TRIALS
         print("Average reward in {} episodes is {}".format(n_trials, avg_rew))
         assert avg_rew > min_avg_reward, \
@@ -54,12 +60,14 @@ def rollout(env, model, n_trials):
     rewards = []
     actions = []
     observations = []
+
     for i in range(n_trials):
         obs = env.reset()
         state = model.initial_state if hasattr(model, 'initial_state') else None
         episode_rew = []
         episode_actions = []
         episode_obs = []
+
         while True:
             if state is not None:
                 a, v, state, _ = model.step(obs, S=state, M=[False])
@@ -67,26 +75,17 @@ def rollout(env, model, n_trials):
                 a,v, _, _ = model.step(obs)
 
             obs, rew, done, _ = env.step(a)
+
             episode_rew.append(rew)
             episode_actions.append(a)
             episode_obs.append(obs)
+
             if done:
                 break
+
         rewards.append(episode_rew)
         actions.append(episode_actions)
         observations.append(episode_obs)
+
     return observations, actions, rewards
 
-
-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/tf_util.py

@@ -1,3 +1,4 @@
+import joblib
 import numpy as np
 import tensorflow as tf  # pylint: ignore-module
 import copy
@@ -305,17 +306,12 @@ def display_var_info(vars):
     logger.info("Total model parameters: %0.2f million" % (count_params*1e-6))
 
 
-def get_available_gpus(session_config=None):
-    # based on recipe from https://stackoverflow.com/a/38580201
-
-    # Unless we allocate a session here, subsequent attempts to create one
-    # will ignore our custom config (in particular, allow_growth=True will have
-    # no effect).
-    if session_config is None:
-        session_config = get_session()._config
+def get_available_gpus():
+    # recipe from here:
+    # https://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
 
     from tensorflow.python.client import device_lib
-    local_device_protos = device_lib.list_local_devices(session_config)
+    local_device_protos = device_lib.list_local_devices()
     return [x.name for x in local_device_protos if x.device_type == 'GPU']
 
 # ================================================================
@@ -343,7 +339,6 @@ def save_state(fname, sess=None):
 # TODO: ensure there is no subtle differences and remove one
 
 def save_variables(save_path, variables=None, sess=None):
-    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 
@@ -355,7 +350,6 @@ def save_variables(save_path, variables=None, sess=None):
     joblib.dump(save_dict, save_path)
 
 def load_variables(load_path, variables=None, sess=None):
-    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 

baselines/common/vec_env/__init__.py

@@ -1,10 +1,185 @@
-from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, CloudpickleWrapper
-from .dummy_vec_env import DummyVecEnv
-from .shmem_vec_env import ShmemVecEnv
-from .subproc_vec_env import SubprocVecEnv
-from .vec_frame_stack import VecFrameStack
-from .vec_monitor import VecMonitor
-from .vec_normalize import VecNormalize
-from .vec_remove_dict_obs import VecExtractDictObs
+from abc import ABC, abstractmethod
+from baselines.common.tile_images import tile_images
 
-__all__ = ['AlreadySteppingError', 'NotSteppingError', 'VecEnv', 'VecEnvWrapper', 'VecEnvObservationWrapper', 'CloudpickleWrapper', 'DummyVecEnv', 'ShmemVecEnv', 'SubprocVecEnv', 'VecFrameStack', 'VecMonitor', 'VecNormalize', 'VecExtractDictObs']
+class AlreadySteppingError(Exception):
+    """
+    Raised when an asynchronous step is running while
+    step_async() is called again.
+    """
+
+    def __init__(self):
+        msg = 'already running an async step'
+        Exception.__init__(self, msg)
+
+
+class NotSteppingError(Exception):
+    """
+    Raised when an asynchronous step is not running but
+    step_wait() is called.
+    """
+
+    def __init__(self):
+        msg = 'not running an async step'
+        Exception.__init__(self, msg)
+
+
+class VecEnv(ABC):
+    """
+    An abstract asynchronous, vectorized environment.
+    Used to batch data from multiple copies of an environment, so that
+    each observation becomes an batch of observations, and expected action is a batch of actions to
+    be applied per-environment.
+    """
+    closed = False
+    viewer = None
+
+    metadata = {
+        'render.modes': ['human', 'rgb_array']
+    }
+
+    def __init__(self, num_envs, observation_space, action_space):
+        self.num_envs = num_envs
+        self.observation_space = observation_space
+        self.action_space = action_space
+
+    @abstractmethod
+    def reset(self):
+        """
+        Reset all the environments and return an array of
+        observations, or a dict of observation arrays.
+
+        If step_async is still doing work, that work will
+        be cancelled and step_wait() should not be called
+        until step_async() is invoked again.
+        """
+        pass
+
+    @abstractmethod
+    def step_async(self, actions):
+        """
+        Tell all the environments to start taking a step
+        with the given actions.
+        Call step_wait() to get the results of the step.
+
+        You should not call this if a step_async run is
+        already pending.
+        """
+        pass
+
+    @abstractmethod
+    def step_wait(self):
+        """
+        Wait for the step taken with step_async().
+
+        Returns (obs, rews, dones, infos):
+         - obs: an array of observations, or a dict of
+                arrays of observations.
+         - rews: an array of rewards
+         - dones: an array of "episode done" booleans
+         - infos: a sequence of info objects
+        """
+        pass
+
+    def close_extras(self):
+        """
+        Clean up the  extra resources, beyond what's in this base class.
+        Only runs when not self.closed.
+        """
+        pass
+
+    def close(self):
+        if self.closed:
+            return
+        if self.viewer is not None:
+            self.viewer.close()
+        self.close_extras()
+        self.closed = True
+
+    def step(self, actions):
+        """
+        Step the environments synchronously.
+
+        This is available for backwards compatibility.
+        """
+        self.step_async(actions)
+        return self.step_wait()
+
+    def render(self, mode='human'):
+        imgs = self.get_images()
+        bigimg = tile_images(imgs)
+        if mode == 'human':
+            self.get_viewer().imshow(bigimg)
+            return self.get_viewer().isopen
+        elif mode == 'rgb_array':
+            return bigimg
+        else:
+            raise NotImplementedError
+
+    def get_images(self):
+        """
+        Return RGB images from each environment
+        """
+        raise NotImplementedError
+
+    @property
+    def unwrapped(self):
+        if isinstance(self, VecEnvWrapper):
+            return self.venv.unwrapped
+        else:
+            return self
+
+    def get_viewer(self):
+        if self.viewer is None:
+            from gym.envs.classic_control import rendering
+            self.viewer = rendering.SimpleImageViewer()
+        return self.viewer
+
+
+class VecEnvWrapper(VecEnv):
+    """
+    An environment wrapper that applies to an entire batch
+    of environments at once.
+    """
+
+    def __init__(self, venv, observation_space=None, action_space=None):
+        self.venv = venv
+        VecEnv.__init__(self,
+                        num_envs=venv.num_envs,
+                        observation_space=observation_space or venv.observation_space,
+                        action_space=action_space or venv.action_space)
+
+    def step_async(self, actions):
+        self.venv.step_async(actions)
+
+    @abstractmethod
+    def reset(self):
+        pass
+
+    @abstractmethod
+    def step_wait(self):
+        pass
+
+    def close(self):
+        return self.venv.close()
+
+    def render(self, mode='human'):
+        return self.venv.render(mode=mode)
+
+    def get_images(self):
+        return self.venv.get_images()
+
+class CloudpickleWrapper(object):
+    """
+    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
+    """
+
+    def __init__(self, x):
+        self.x = x
+
+    def __getstate__(self):
+        import cloudpickle
+        return cloudpickle.dumps(self.x)
+
+    def __setstate__(self, ob):
+        import pickle
+        self.x = pickle.loads(ob)

baselines/common/vec_env/dummy_vec_env.py

@@ -1,5 +1,6 @@
 import numpy as np
-from .vec_env import VecEnv
+from gym import spaces
+from . import VecEnv
 from .util import copy_obs_dict, dict_to_obs, obs_space_info
 
 class DummyVecEnv(VecEnv):
@@ -26,7 +27,7 @@ class DummyVecEnv(VecEnv):
         self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
         self.buf_infos = [{} for _ in range(self.num_envs)]
         self.actions = None
-        self.spec = self.envs[0].spec
+        self.specs = [e.spec for e in self.envs]
 
     def step_async(self, actions):
         listify = True
@@ -45,8 +46,8 @@ class DummyVecEnv(VecEnv):
     def step_wait(self):
         for e in range(self.num_envs):
             action = self.actions[e]
-            # if isinstance(self.envs[e].action_space, spaces.Discrete):
-            #    action = int(action)
+            if isinstance(self.envs[e].action_space, spaces.Discrete):
+                action = int(action)
 
             obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
             if self.buf_dones[e]:

baselines/common/vec_env/shmem_vec_env.py

@@ -2,9 +2,9 @@
 An interface for asynchronous vectorized environments.
 """
 
-import multiprocessing as mp
+from multiprocessing import Pipe, Array, Process
 import numpy as np
-from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
+from . import VecEnv, CloudpickleWrapper
 import ctypes
 from baselines import logger
 
@@ -22,12 +22,11 @@ class ShmemVecEnv(VecEnv):
     Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
     """
 
-    def __init__(self, env_fns, spaces=None, context='spawn'):
+    def __init__(self, env_fns, spaces=None):
         """
         If you don't specify observation_space, we'll have to create a dummy
         environment to get it.
         """
-        ctx = mp.get_context(context)
         if spaces:
             observation_space, action_space = spaces
         else:
@@ -40,22 +39,22 @@ class ShmemVecEnv(VecEnv):
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
         self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
         self.obs_bufs = [
-            {k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
+            {k: Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
             for _ in env_fns]
         self.parent_pipes = []
         self.procs = []
-        with clear_mpi_env_vars():
-            for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
-                wrapped_fn = CloudpickleWrapper(env_fn)
-                parent_pipe, child_pipe = ctx.Pipe()
-                proc = ctx.Process(target=_subproc_worker,
-                            args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
-                proc.daemon = True
-                self.procs.append(proc)
-                self.parent_pipes.append(parent_pipe)
-                proc.start()
-                child_pipe.close()
+        for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
+            wrapped_fn = CloudpickleWrapper(env_fn)
+            parent_pipe, child_pipe = Pipe()
+            proc = Process(target=_subproc_worker,
+                           args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
+            proc.daemon = True
+            self.procs.append(proc)
+            self.parent_pipes.append(parent_pipe)
+            proc.start()
+            child_pipe.close()
         self.waiting_step = False
+        self.specs = [f().spec for f in env_fns]
         self.viewer = None
 
     def reset(self):

baselines/common/vec_env/subproc_vec_env.py

@@ -1,8 +1,6 @@
-import multiprocessing as mp
-
 import numpy as np
-from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
-
+from multiprocessing import Process, Pipe
+from . import VecEnv, CloudpickleWrapper
 
 def worker(remote, parent_remote, env_fn_wrapper):
     parent_remote.close()
@@ -23,8 +21,8 @@ def worker(remote, parent_remote, env_fn_wrapper):
             elif cmd == 'close':
                 remote.close()
                 break
-            elif cmd == 'get_spaces_spec':
-                remote.send((env.observation_space, env.action_space, env.spec))
+            elif cmd == 'get_spaces':
+                remote.send((env.observation_space, env.action_space))
             else:
                 raise NotImplementedError
     except KeyboardInterrupt:
@@ -38,7 +36,7 @@ class SubprocVecEnv(VecEnv):
     VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
     Recommended to use when num_envs > 1 and step() can be a bottleneck.
     """
-    def __init__(self, env_fns, spaces=None, context='spawn'):
+    def __init__(self, env_fns, spaces=None):
         """
         Arguments:
 
@@ -47,20 +45,19 @@ class SubprocVecEnv(VecEnv):
         self.waiting = False
         self.closed = False
         nenvs = len(env_fns)
-        ctx = mp.get_context(context)
-        self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(nenvs)])
-        self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
+        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
+        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
                    for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
         for p in self.ps:
             p.daemon = True  # if the main process crashes, we should not cause things to hang
-            with clear_mpi_env_vars():
-                p.start()
+            p.start()
         for remote in self.work_remotes:
             remote.close()
 
-        self.remotes[0].send(('get_spaces_spec', None))
-        observation_space, action_space, self.spec = self.remotes[0].recv()
+        self.remotes[0].send(('get_spaces', None))
+        observation_space, action_space = self.remotes[0].recv()
         self.viewer = None
+        self.specs = [f().spec for f in env_fns]
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
 
     def step_async(self, actions):
@@ -102,16 +99,16 @@ class SubprocVecEnv(VecEnv):
     def _assert_not_closed(self):
         assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"
 
-    def __del__(self):
-        if not self.closed:
-            self.close()
 
 def _flatten_obs(obs):
-    assert isinstance(obs, (list, tuple))
+    assert isinstance(obs, list) or isinstance(obs, tuple)
     assert len(obs) > 0
 
     if isinstance(obs[0], dict):
+        import collections
+        assert isinstance(obs, collections.OrderedDict)
         keys = obs[0].keys()
         return {k: np.stack([o[k] for o in obs]) for k in keys}
     else:
         return np.stack(obs)
+

baselines/common/vec_env/test_vec_env.py

@@ -8,40 +8,39 @@ import pytest
 from .dummy_vec_env import DummyVecEnv
 from .shmem_vec_env import ShmemVecEnv
 from .subproc_vec_env import SubprocVecEnv
-from baselines.common.tests.test_with_mpi import with_mpi
 
 
-def assert_venvs_equal(venv1, venv2, num_steps):
+def assert_envs_equal(env1, env2, num_steps):
     """
     Compare two environments over num_steps steps and make sure
     that the observations produced by each are the same when given
     the same actions.
     """
-    assert venv1.num_envs == venv2.num_envs
-    assert venv1.observation_space.shape == venv2.observation_space.shape
-    assert venv1.observation_space.dtype == venv2.observation_space.dtype
-    assert venv1.action_space.shape == venv2.action_space.shape
-    assert venv1.action_space.dtype == venv2.action_space.dtype
+    assert env1.num_envs == env2.num_envs
+    assert env1.action_space.shape == env2.action_space.shape
+    assert env1.action_space.dtype == env2.action_space.dtype
+    joint_shape = (env1.num_envs,) + env1.action_space.shape
 
     try:
-        obs1, obs2 = venv1.reset(), venv2.reset()
+        obs1, obs2 = env1.reset(), env2.reset()
         assert np.array(obs1).shape == np.array(obs2).shape
-        assert np.array(obs1).shape == (venv1.num_envs,) + venv1.observation_space.shape
+        assert np.array(obs1).shape == joint_shape
         assert np.allclose(obs1, obs2)
-        venv1.action_space.seed(1337)
+        np.random.seed(1337)
         for _ in range(num_steps):
-            actions = np.array([venv1.action_space.sample() for _ in range(venv1.num_envs)])
-            for venv in [venv1, venv2]:
-                venv.step_async(actions)
-            outs1 = venv1.step_wait()
-            outs2 = venv2.step_wait()
+            actions = np.array(np.random.randint(0, 0x100, size=joint_shape),
+                               dtype=env1.action_space.dtype)
+            for env in [env1, env2]:
+                env.step_async(actions)
+            outs1 = env1.step_wait()
+            outs2 = env2.step_wait()
             for out1, out2 in zip(outs1[:3], outs2[:3]):
                 assert np.array(out1).shape == np.array(out2).shape
                 assert np.allclose(out1, out2)
             assert list(outs1[3]) == list(outs2[3])
     finally:
-        venv1.close()
-        venv2.close()
+        env1.close()
+        env2.close()
 
 
 @pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv))
@@ -64,7 +63,7 @@ def test_vec_env(klass, dtype):  # pylint: disable=R0914
     fns = [make_fn(i) for i in range(num_envs)]
     env1 = DummyVecEnv(fns)
     env2 = klass(fns)
-    assert_venvs_equal(env1, env2, num_steps=num_steps)
+    assert_envs_equal(env1, env2, num_steps=num_steps)
 
 
 class SimpleEnv(gym.Env):
@@ -100,15 +99,3 @@ class SimpleEnv(gym.Env):
 
     def render(self, mode=None):
         raise NotImplementedError
-
-
-
-@with_mpi()
-def test_mpi_with_subprocvecenv():
-    shape = (2,3,4)
-    nenv = 1
-    venv = SubprocVecEnv([lambda: SimpleEnv(0, shape, 'float32')] * nenv)
-    ob = venv.reset()
-    venv.close()
-    assert ob.shape == (nenv,) + shape
-

baselines/common/vec_env/vec_env.py

@@ -1,223 +0,0 @@
-import contextlib
-import os
-from abc import ABC, abstractmethod
-
-from baselines.common.tile_images import tile_images
-
-class AlreadySteppingError(Exception):
-    """
-    Raised when an asynchronous step is running while
-    step_async() is called again.
-    """
-
-    def __init__(self):
-        msg = 'already running an async step'
-        Exception.__init__(self, msg)
-
-
-class NotSteppingError(Exception):
-    """
-    Raised when an asynchronous step is not running but
-    step_wait() is called.
-    """
-
-    def __init__(self):
-        msg = 'not running an async step'
-        Exception.__init__(self, msg)
-
-
-class VecEnv(ABC):
-    """
-    An abstract asynchronous, vectorized environment.
-    Used to batch data from multiple copies of an environment, so that
-    each observation becomes an batch of observations, and expected action is a batch of actions to
-    be applied per-environment.
-    """
-    closed = False
-    viewer = None
-
-    metadata = {
-        'render.modes': ['human', 'rgb_array']
-    }
-
-    def __init__(self, num_envs, observation_space, action_space):
-        self.num_envs = num_envs
-        self.observation_space = observation_space
-        self.action_space = action_space
-
-    @abstractmethod
-    def reset(self):
-        """
-        Reset all the environments and return an array of
-        observations, or a dict of observation arrays.
-
-        If step_async is still doing work, that work will
-        be cancelled and step_wait() should not be called
-        until step_async() is invoked again.
-        """
-        pass
-
-    @abstractmethod
-    def step_async(self, actions):
-        """
-        Tell all the environments to start taking a step
-        with the given actions.
-        Call step_wait() to get the results of the step.
-
-        You should not call this if a step_async run is
-        already pending.
-        """
-        pass
-
-    @abstractmethod
-    def step_wait(self):
-        """
-        Wait for the step taken with step_async().
-
-        Returns (obs, rews, dones, infos):
-         - obs: an array of observations, or a dict of
-                arrays of observations.
-         - rews: an array of rewards
-         - dones: an array of "episode done" booleans
-         - infos: a sequence of info objects
-        """
-        pass
-
-    def close_extras(self):
-        """
-        Clean up the  extra resources, beyond what's in this base class.
-        Only runs when not self.closed.
-        """
-        pass
-
-    def close(self):
-        if self.closed:
-            return
-        if self.viewer is not None:
-            self.viewer.close()
-        self.close_extras()
-        self.closed = True
-
-    def step(self, actions):
-        """
-        Step the environments synchronously.
-
-        This is available for backwards compatibility.
-        """
-        self.step_async(actions)
-        return self.step_wait()
-
-    def render(self, mode='human'):
-        imgs = self.get_images()
-        bigimg = tile_images(imgs)
-        if mode == 'human':
-            self.get_viewer().imshow(bigimg)
-            return self.get_viewer().isopen
-        elif mode == 'rgb_array':
-            return bigimg
-        else:
-            raise NotImplementedError
-
-    def get_images(self):
-        """
-        Return RGB images from each environment
-        """
-        raise NotImplementedError
-
-    @property
-    def unwrapped(self):
-        if isinstance(self, VecEnvWrapper):
-            return self.venv.unwrapped
-        else:
-            return self
-
-    def get_viewer(self):
-        if self.viewer is None:
-            from gym.envs.classic_control import rendering
-            self.viewer = rendering.SimpleImageViewer()
-        return self.viewer
-
-class VecEnvWrapper(VecEnv):
-    """
-    An environment wrapper that applies to an entire batch
-    of environments at once.
-    """
-
-    def __init__(self, venv, observation_space=None, action_space=None):
-        self.venv = venv
-        super().__init__(num_envs=venv.num_envs,
-                        observation_space=observation_space or venv.observation_space,
-                        action_space=action_space or venv.action_space)
-
-    def step_async(self, actions):
-        self.venv.step_async(actions)
-
-    @abstractmethod
-    def reset(self):
-        pass
-
-    @abstractmethod
-    def step_wait(self):
-        pass
-
-    def close(self):
-        return self.venv.close()
-
-    def render(self, mode='human'):
-        return self.venv.render(mode=mode)
-
-    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):
-        pass
-
-    def reset(self):
-        obs = self.venv.reset()
-        return self.process(obs)
-
-    def step_wait(self):
-        obs, rews, dones, infos = self.venv.step_wait()
-        return self.process(obs), rews, dones, infos
-
-class CloudpickleWrapper(object):
-    """
-    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
-    """
-
-    def __init__(self, x):
-        self.x = x
-
-    def __getstate__(self):
-        import cloudpickle
-        return cloudpickle.dumps(self.x)
-
-    def __setstate__(self, ob):
-        import pickle
-        self.x = pickle.loads(ob)
-
-
-@contextlib.contextmanager
-def clear_mpi_env_vars():
-    """
-    from mpi4py import MPI will call MPI_Init by default.  If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
-    This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
-    Processes.
-    """
-    removed_environment = {}
-    for k, v in list(os.environ.items()):
-        for prefix in ['OMPI_', 'PMI_']:
-            if k.startswith(prefix):
-                removed_environment[k] = v
-                del os.environ[k]
-    try:
-        yield
-    finally:
-        os.environ.update(removed_environment)

baselines/common/vec_env/vec_frame_stack.py

@@ -1,4 +1,4 @@
-from .vec_env import VecEnvWrapper
+from . import VecEnvWrapper
 import numpy as np
 from gym import spaces
 

baselines/common/vec_env/vec_monitor.py

@@ -2,25 +2,15 @@ from . import VecEnvWrapper
 from baselines.bench.monitor import ResultsWriter
 import numpy as np
 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):
         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)
-        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)
-            self.eplen_buf = deque([], maxlen=keep_buf)
+        self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
 
     def reset(self):
         obs = self.venv.reset()
@@ -32,24 +22,16 @@ 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)
-                    self.eplen_buf.append(eplen)
-                self.epcount += 1
                 self.eprets[i] = 0
                 self.eplens[i] = 0
-                if self.results_writer:
-                    self.results_writer.write_row(epinfo)
-                newinfos[i] = info
+                self.results_writer.write_row(epinfo)
+
+            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

@@ -1,10 +0,0 @@
-from .vec_env import VecEnvObservationWrapper
-
-class VecExtractDictObs(VecEnvObservationWrapper):
-    def __init__(self, venv, key):
-        self.key = key
-        super().__init__(venv=venv,
-            observation_space=venv.observation_space.spaces[self.key])
-
-    def process(self, obs):
-        return obs[self.key]

baselines/common/wrappers.py

@@ -1,29 +0,0 @@
-import gym
-
-class TimeLimit(gym.Wrapper):
-    def __init__(self, env, max_episode_steps=None):
-        super(TimeLimit, self).__init__(env)
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = 0
-
-    def step(self, ac):
-        observation, reward, done, info = self.env.step(ac)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            done = True
-            info['TimeLimit.truncated'] = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
-
-class 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)

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/ddpg_learner.py

@@ -17,7 +17,7 @@ except ImportError:
 def normalize(x, stats):
     if stats is None:
         return x
-    return (x - stats.mean) / (stats.std + 1e-8)
+    return (x - stats.mean) / stats.std
 
 
 def denormalize(x, stats):

baselines/ddpg/test_smoke.py

@@ -1,6 +1,7 @@
-from baselines.common.tests.util import smoketest
+from baselines.run import main as M
+
 def _run(argstr):
-    smoketest('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr)
+    M(('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr).split(' '))
 
 def test_popart():
     _run('--normalize_returns=True --popart=True')

baselines/deepq/experiments/custom_cartpole.py

@@ -23,7 +23,7 @@ def model(inpt, num_actions, scope, reuse=False):
 
 
 if __name__ == '__main__':
-    with U.make_session(num_cpu=8):
+    with U.make_session(8):
         # Create the environment
         env = gym.make("CartPole-v0")
         # Create all the functions necessary to train the model

baselines/deepq/utils.py

@@ -20,7 +20,7 @@ class TfInput(object):
         """
         raise NotImplementedError
 
-    def make_feed_dict(self, data):
+    def make_feed_dict(data):
         """Given data input it to the placeholder(s)."""
         raise NotImplementedError
 

baselines/gail/README.md

@@ -12,13 +12,13 @@ Download the expert data into `./data`, [download link](https://drive.google.com
 
 ### Step 2: Run GAIL
 
-Run with single rank:
+Run with single thread:
 
 ```bash
 python -m baselines.gail.run_mujoco
 ```
 
-Run with multiple ranks:
+Run with multiple threads:
 
 ```bash
 mpirun -np 16 python -m baselines.gail.run_mujoco

baselines/gail/adversary.py

@@ -66,7 +66,7 @@ class TransitionClassifier(object):
 
             with tf.variable_scope("obfilter"):
                 self.obs_rms = RunningMeanStd(shape=self.observation_shape)
-            obs = (obs_ph - self.obs_rms.mean) / self.obs_rms.std
+            obs = (obs_ph - self.obs_rms.mean / self.obs_rms.std)
             _input = tf.concat([obs, acs_ph], axis=1)  # concatenate the two input -> form a transition
             p_h1 = tf.contrib.layers.fully_connected(_input, self.hidden_size, activation_fn=tf.nn.tanh)
             p_h2 = tf.contrib.layers.fully_connected(p_h1, self.hidden_size, activation_fn=tf.nn.tanh)

baselines/gail/dataset/mujoco_dset.py

@@ -50,12 +50,8 @@ class Mujoco_Dset(object):
         # obs, acs: shape (N, L, ) + S where N = # episodes, L = episode length
         # and S is the environment observation/action space.
         # Flatten to (N * L, prod(S))
-        if len(obs.shape) > 2:
-            self.obs = np.reshape(obs, [-1, np.prod(obs.shape[2:])])
-            self.acs = np.reshape(acs, [-1, np.prod(acs.shape[2:])])
-        else:
-            self.obs = np.vstack(obs)
-            self.acs = np.vstack(acs)
+        self.obs = np.reshape(obs, [-1, np.prod(obs.shape[2:])])
+        self.acs = np.reshape(acs, [-1, np.prod(acs.shape[2:])])
 
         self.rets = traj_data['ep_rets'][:traj_limitation]
         self.avg_ret = sum(self.rets)/len(self.rets)

baselines/her/her.py

@@ -108,7 +108,7 @@ def learn(*, network, env, total_timesteps,
 
     # Prepare params.
     params = config.DEFAULT_PARAMS
-    env_name = env.spec.id
+    env_name = env.specs[0].id
     params['env_name'] = env_name
     params['replay_strategy'] = replay_strategy
     if env_name in config.DEFAULT_ENV_PARAMS:

baselines/logger.py

@@ -7,7 +7,6 @@ import time
 import datetime
 import tempfile
 from collections import defaultdict
-from contextlib import contextmanager
 
 DEBUG = 10
 INFO = 20
@@ -38,8 +37,8 @@ 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)
@@ -69,8 +68,7 @@ class HumanOutputFormat(KVWriter, SeqWriter):
         self.file.flush()
 
     def _truncate(self, s):
-        maxlen = 30
-        return s[:maxlen-3] + '...' if len(s) > maxlen else s
+        return s[:20] + '...' if len(s) > 23 else s
 
     def writeseq(self, seq):
         seq = list(seq)
@@ -92,6 +90,7 @@ 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()
@@ -196,13 +195,13 @@ def logkv(key, val):
     Call this once for each diagnostic quantity, each iteration
     If called many times, last value will be used.
     """
-    get_current().logkv(key, val)
+    Logger.CURRENT.logkv(key, val)
 
 def logkv_mean(key, val):
     """
     The same as logkv(), but if called many times, values averaged.
     """
-    get_current().logkv_mean(key, val)
+    Logger.CURRENT.logkv_mean(key, val)
 
 def logkvs(d):
     """
@@ -214,18 +213,21 @@ def logkvs(d):
 def dumpkvs():
     """
     Write all of the diagnostics from the current iteration
+
+    level: int. (see logger.py docs) If the global logger level is higher than
+                the level argument here, don't print to stdout.
     """
-    return get_current().dumpkvs()
+    Logger.CURRENT.dumpkvs()
 
 def getkvs():
-    return get_current().name2val
+    return Logger.CURRENT.name2val
 
 
 def log(*args, level=INFO):
     """
     Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
     """
-    get_current().log(*args, level=level)
+    Logger.CURRENT.log(*args, level=level)
 
 def debug(*args):
     log(*args, level=DEBUG)
@@ -244,29 +246,30 @@ def set_level(level):
     """
     Set logging threshold on current logger.
     """
-    get_current().set_level(level)
-
-def set_comm(comm):
-    get_current().set_comm(comm)
+    Logger.CURRENT.set_level(level)
 
 def get_dir():
     """
     Get directory that log files are being written to.
     will be None if there is no output directory (i.e., if you didn't call start)
     """
-    return get_current().get_dir()
+    return Logger.CURRENT.get_dir()
 
 record_tabular = logkv
 dump_tabular = dumpkvs
 
-@contextmanager
-def profile_kv(scopename):
-    logkey = 'wait_' + scopename
-    tstart = time.time()
-    try:
-        yield
-    finally:
-        get_current().name2val[logkey] += time.time() - tstart
+class ProfileKV:
+    """
+    Usage:
+    with logger.ProfileKV("interesting_scope"):
+        code
+    """
+    def __init__(self, n):
+        self.n = "wait_" + n
+    def __enter__(self):
+        self.t1 = time.time()
+    def __exit__(self ,type, value, traceback):
+        Logger.CURRENT.name2val[self.n] += time.time() - self.t1
 
 def profile(n):
     """
@@ -276,7 +279,7 @@ def profile(n):
     """
     def decorator_with_name(func):
         def func_wrapper(*args, **kwargs):
-            with profile_kv(n):
+            with ProfileKV(n):
                 return func(*args, **kwargs)
         return func_wrapper
     return decorator_with_name
@@ -286,25 +289,17 @@ def profile(n):
 # Backend
 # ================================================================
 
-def get_current():
-    if Logger.CURRENT is None:
-        _configure_default_logger()
-
-    return Logger.CURRENT
-
-
 class Logger(object):
     DEFAULT = None  # A logger with no output files. (See right below class definition)
                     # So that you can still log to the terminal without setting up any output files
     CURRENT = None  # Current logger being used by the free functions above
 
-    def __init__(self, dir, output_formats, comm=None):
+    def __init__(self, dir, output_formats):
         self.name2val = defaultdict(float)  # values this iteration
         self.name2cnt = defaultdict(int)
         self.level = INFO
         self.dir = dir
         self.output_formats = output_formats
-        self.comm = comm
 
     # Logging API, forwarded
     # ----------------------------------------
@@ -312,27 +307,20 @@ class Logger(object):
         self.name2val[key] = val
 
     def logkv_mean(self, key, val):
+        if val is None:
+            self.name2val[key] = None
+            return
         oldval, cnt = self.name2val[key], self.name2cnt[key]
         self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1)
         self.name2cnt[key] = cnt + 1
 
     def dumpkvs(self):
-        if self.comm is None:
-            d = self.name2val
-        else:
-            from baselines.common import mpi_util
-            d = mpi_util.mpi_weighted_mean(self.comm,
-                {name : (val, self.name2cnt.get(name, 1))
-                    for (name, val) in self.name2val.items()})
-            if self.comm.rank != 0:
-                d['dummy'] = 1 # so we don't get a warning about empty dict
-        out = d.copy() # Return the dict for unit testing purposes
+        if self.level == DISABLED: return
         for fmt in self.output_formats:
             if isinstance(fmt, KVWriter):
-                fmt.writekvs(d)
+                fmt.writekvs(self.name2val)
         self.name2val.clear()
         self.name2cnt.clear()
-        return out
 
     def log(self, *args, level=INFO):
         if self.level <= level:
@@ -343,9 +331,6 @@ class Logger(object):
     def set_level(self, level):
         self.level = level
 
-    def set_comm(self, comm):
-        self.comm = comm
-
     def get_dir(self):
         return self.dir
 
@@ -360,19 +345,7 @@ 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=''):
-    """
-    If comm is provided, average all numerical stats across that comm
-    """
+def configure(dir=None, format_strs=None):
     if dir is None:
         dir = os.getenv('OPENAI_LOGDIR')
     if dir is None:
@@ -381,9 +354,15 @@ 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:
@@ -393,11 +372,15 @@ def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
     format_strs = filter(None, format_strs)
     output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
 
-    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
+    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats)
     log('Logging to %s'%dir)
 
 def _configure_default_logger():
-    configure()
+    format_strs = None
+    # keep the old default of only writing to stdout
+    if 'OPENAI_LOG_FORMAT' not in os.environ:
+        format_strs = ['stdout']
+    configure(format_strs=format_strs)
     Logger.DEFAULT = Logger.CURRENT
 
 def reset():
@@ -406,15 +389,17 @@ def reset():
         Logger.CURRENT = Logger.DEFAULT
         log('Reset logger')
 
-@contextmanager
-def scoped_configure(dir=None, format_strs=None, comm=None):
-    prevlogger = Logger.CURRENT
-    configure(dir=dir, format_strs=format_strs, comm=comm)
-    try:
-        yield
-    finally:
+class scoped_configure(object):
+    def __init__(self, dir=None, format_strs=None):
+        self.dir = dir
+        self.format_strs = format_strs
+        self.prevlogger = None
+    def __enter__(self):
+        self.prevlogger = Logger.CURRENT
+        configure(dir=self.dir, format_strs=self.format_strs)
+    def __exit__(self, *args):
         Logger.CURRENT.close()
-        Logger.CURRENT = prevlogger
+        Logger.CURRENT = self.prevlogger
 
 # ================================================================
 
@@ -438,7 +423,7 @@ def _demo():
     logkv_mean("b", -44.4)
     logkv("a", 5.5)
     dumpkvs()
-    info("^^^ should see b = -33.3")
+    info("^^^ should see b = 33.3")
 
     logkv("b", -2.5)
     dumpkvs()
@@ -471,6 +456,7 @@ def read_tb(path):
     import pandas
     import numpy as np
     from glob import glob
+    from collections import defaultdict
     import tensorflow as tf
     if osp.isdir(path):
         fnames = glob(osp.join(path, "events.*"))
@@ -496,5 +482,8 @@ def read_tb(path):
             data[step-1, colidx] = value
     return pandas.DataFrame(data, columns=tags)
 
+# configure the default logger on import
+_configure_default_logger()
+
 if __name__ == "__main__":
     _demo()

baselines/ppo1/pposgd_simple.py

@@ -97,6 +97,7 @@ def learn(env, policy_fn, *,
     ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
 
     lrmult = tf.placeholder(name='lrmult', dtype=tf.float32, shape=[]) # learning rate multiplier, updated with schedule
+    clip_param = clip_param * lrmult # Annealed clipping parameter epsilon
 
     ob = U.get_placeholder_cached(name="ob")
     ac = pi.pdtype.sample_placeholder([None])
@@ -167,7 +168,7 @@ def learn(env, policy_fn, *,
         ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
         vpredbefore = seg["vpred"] # predicted value function before udpate
         atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
-        d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret), deterministic=pi.recurrent)
+        d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret), shuffle=not pi.recurrent)
         optim_batchsize = optim_batchsize or ob.shape[0]
 
         if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy

baselines/ppo1/run_humanoid.py

@@ -19,17 +19,16 @@ def train(num_timesteps, seed, model_path=None):
     # these are good enough to make humanoid walk, but whether those are
     # an absolute best or not is not certain
     env = RewScale(env, 0.1)
-    logger.log("NOTE: reward will be scaled by a factor of 10  in logged stats. Check the monitor for unscaled reward.")
     pi = pposgd_simple.learn(env, policy_fn,
             max_timesteps=num_timesteps,
             timesteps_per_actorbatch=2048,
-            clip_param=0.1, entcoeff=0.0,
+            clip_param=0.2, entcoeff=0.0,
             optim_epochs=10,
-            optim_stepsize=1e-4,
+            optim_stepsize=3e-4,
             optim_batchsize=64,
             gamma=0.99,
             lam=0.95,
-            schedule='constant',
+            schedule='linear',
         )
     env.close()
     if model_path:
@@ -48,7 +47,7 @@ def main():
     logger.configure()
     parser = mujoco_arg_parser()
     parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy'))
-    parser.set_defaults(num_timesteps=int(5e7))
+    parser.set_defaults(num_timesteps=int(2e7))
 
     args = parser.parse_args()
 
@@ -69,5 +68,8 @@ def main():
             if done:
                 ob = env.reset()
 
+
+
+
 if __name__ == '__main__':
     main()

baselines/ppo2/defaults.py

@@ -18,7 +18,7 @@ def atari():
         lam=0.95, gamma=0.99, noptepochs=4, log_interval=1,
         ent_coef=.01,
         lr=lambda f : f * 2.5e-4,
-        cliprange=0.1,
+        cliprange=lambda f : f * 0.1,
     )
 
 def retro():

baselines/ppo2/microbatched_model.py

@@ -8,7 +8,7 @@ class MicrobatchedModel(Model):
     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
@@ -23,9 +23,7 @@ class MicrobatchedModel(Model):
                 nsteps=nsteps,
                 ent_coef=ent_coef,
                 vf_coef=vf_coef,
-                max_grad_norm=max_grad_norm,
-                mpi_rank_weight=mpi_rank_weight,
-                comm=comm)
+                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))

baselines/ppo2/model.py

@@ -25,12 +25,9 @@ class Model(object):
     - 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):
+                nsteps, ent_coef, vf_coef, max_grad_norm, microbatch_size=None):
         self.sess = sess = get_session()
 
-        if MPI is not None and comm is None:
-            comm = MPI.COMM_WORLD
-
         with tf.variable_scope('ppo2_model', reuse=tf.AUTO_REUSE):
             # CREATE OUR TWO MODELS
             # act_model that is used for sampling
@@ -94,8 +91,8 @@ class Model(object):
         # 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)
+        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
@@ -128,7 +125,7 @@ class Model(object):
         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
+            sync_from_root(sess, global_variables) #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)

baselines/ppo2/ppo2.py

@@ -21,7 +21,7 @@ def constfn(val):
 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):
+            save_interval=0, load_path=None, model_fn=None, **network_kwargs):
     '''
     Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
 
@@ -97,7 +97,6 @@ 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:
@@ -106,7 +105,7 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
 
     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)
+                    max_grad_norm=max_grad_norm)
 
     if load_path is not None:
         model.load(load_path)
@@ -119,32 +118,24 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
     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()
+    tfirststart = time.time()
 
     nupdates = total_timesteps//nbatch
     for update in range(1, nupdates+1):
         assert nbatch % nminibatches == 0
         # Start timer
-        tstart = time.perf_counter()
+        tstart = time.time()
         frac = 1.0 - (update - 1.0) / nupdates
         # Calculate the learning rate
         lrnow = lr(frac)
         # 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
         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
 
-        if update % log_interval == 0 and is_mpi_root: logger.info('Done.')
-
         epinfobuf.extend(epinfos)
         if eval_env is not None:
             eval_epinfobuf.extend(eval_epinfos)
@@ -169,6 +160,7 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
             envsperbatch = nenvs // nminibatches
             envinds = np.arange(nenvs)
             flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
+            envsperbatch = nbatch_train // nsteps
             for _ in range(noptepochs):
                 np.random.shuffle(envinds)
                 for start in range(0, nenvs, envsperbatch):
@@ -182,39 +174,34 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
         # Feedforward --> get losses --> update
         lossvals = np.mean(mblossvals, axis=0)
         # End timer
-        tnow = time.perf_counter()
+        tnow = time.time()
         # 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)
+            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)
+            logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
-                logger.logkv('loss/' + lossname, lossval)
-
-            logger.dumpkvs()
-        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and is_mpi_root:
+                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)
             print('Saving to', savepath)
             model.save(savepath)
-
     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):

baselines/ppo2/test_microbatches.py

@@ -25,11 +25,10 @@ def test_microbatches():
     env_test = DummyVecEnv([env_fn])
     sess_test = make_session(make_default=True, graph=tf.Graph())
     learn_fn(env=env_test, model_fn=partial(MicrobatchedModel, microbatch_size=2))
-    # learn_fn(env=env_test)
     vars_test = {v.name: sess_test.run(v) for v in tf.trainable_variables()}
 
     for v in vars_ref:
-        np.testing.assert_allclose(vars_ref[v], vars_test[v], atol=3e-3)
+        np.testing.assert_allclose(vars_ref[v], vars_test[v], atol=1e-3)
 
 if __name__ == '__main__':
     test_microbatches()

baselines/run.py

@@ -1,5 +1,4 @@
 import sys
-import re
 import multiprocessing
 import os.path as osp
 import gym
@@ -7,13 +6,15 @@ from collections import defaultdict
 import tensorflow as tf
 import numpy as np
 
-from baselines.common.vec_env import VecFrameStack, VecNormalize, VecEnv
 from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
 from baselines.common.tf_util import get_session
 from baselines import logger
 from importlib import import_module
 
+from baselines.common.vec_env.vec_normalize import VecNormalize
+
 try:
     from mpi4py import MPI
 except ImportError:
@@ -51,7 +52,7 @@ _game_envs['retro'] = {
 
 
 def train(args, extra_args):
-    env_type, env_id = get_env_type(args)
+    env_type, env_id = get_env_type(args.env)
     print('env_type: {}'.format(env_type))
 
     total_timesteps = int(args.num_timesteps)
@@ -63,7 +64,7 @@ def train(args, extra_args):
 
     env = build_env(args)
     if args.save_video_interval != 0:
-        env = VecVideoRecorder(env, osp.join(logger.get_dir(), "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
+        env = VecVideoRecorder(env, osp.join(logger.Logger.CURRENT.dir, "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
 
     if args.network:
         alg_kwargs['network'] = args.network
@@ -90,7 +91,7 @@ def build_env(args):
     alg = args.alg
     seed = args.seed
 
-    env_type, env_id = get_env_type(args)
+    env_type, env_id = get_env_type(args.env)
 
     if env_type in {'atari', 'retro'}:
         if alg == 'deepq':
@@ -103,27 +104,22 @@ def build_env(args):
             env = VecFrameStack(env, frame_stack_size)
 
     else:
-        config = tf.ConfigProto(allow_soft_placement=True,
+       config = tf.ConfigProto(allow_soft_placement=True,
                                intra_op_parallelism_threads=1,
                                inter_op_parallelism_threads=1)
-        config.gpu_options.allow_growth = True
-        get_session(config=config)
+       config.gpu_options.allow_growth = True
+       get_session(config=config)
 
-        flatten_dict_observations = alg not in {'her'}
-        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)
+       flatten_dict_observations = alg not in {'her'}
+       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)
+       if env_type == 'mujoco':
+           env = VecNormalize(env)
 
     return env
 
 
-def get_env_type(args):
-    env_id = args.env
-
-    if args.env_type is not None:
-        return args.env_type, env_id
-
+def get_env_type(env_id):
     # Re-parse the gym registry, since we could have new envs since last time.
     for env in gym.envs.registry.all():
         env_type = env._entry_point.split(':')[0].split('.')[-1]
@@ -138,8 +134,6 @@ def get_env_type(args):
             if env_id in e:
                 env_type = g
                 break
-        if ':' in env_id:
-            env_type = re.sub(r':.*', '', env_id)
         assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys())
 
     return env_type, env_id
@@ -200,6 +194,9 @@ def main(args):
     args, unknown_args = arg_parser.parse_known_args(args)
     extra_args = parse_cmdline_kwargs(unknown_args)
 
+    if args.extra_import is not None:
+        import_module(args.extra_import)
+
     if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
         rank = 0
         logger.configure()
@@ -208,6 +205,7 @@ def main(args):
         rank = MPI.COMM_WORLD.Get_rank()
 
     model, env = train(args, extra_args)
+    env.close()
 
     if args.save_path is not None and rank == 0:
         save_path = osp.expanduser(args.save_path)
@@ -215,28 +213,26 @@ def main(args):
 
     if args.play:
         logger.log("Running trained model")
+        env = build_env(args)
         obs = env.reset()
 
         state = model.initial_state if hasattr(model, 'initial_state') else None
         dones = np.zeros((1,))
 
-        episode_rew = 0
         while True:
             if state is not None:
                 actions, _, state, _ = model.step(obs,S=state, M=dones)
             else:
                 actions, _, _, _ = model.step(obs)
 
-            obs, rew, done, _ = env.step(actions)
-            episode_rew += rew[0] if isinstance(env, VecEnv) else rew
+            obs, _, done, _ = env.step(actions)
             env.render()
             done = done.any() if isinstance(done, np.ndarray) else done
+
             if done:
-                print('episode_rew={}'.format(episode_rew))
-                episode_rew = 0
                 obs = env.reset()
 
-    env.close()
+        env.close()
 
     return model
 

benchmarks_atari10M.htm

@@ -120,7 +120,7 @@
             
             <td>114.26</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -152,7 +152,7 @@
             
             <td>131.46</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -184,7 +184,7 @@
             
             <td>113.58</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -216,7 +216,7 @@
             
             <td>82.94</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -248,7 +248,7 @@
             
             <td>81.61</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -280,7 +280,7 @@
             
             <td>59.72</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         
@@ -312,7 +312,7 @@
             
             <td>14.98</td>
             
-            <td><a href=https://github.com/openai/baselines/commit/7bfbcf177eca8f46c0c0bfbb378e044539f5e061>7bfbcf1</a></td>
+            <td>cbd21ef</td>
             
         </tr>
         

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'