updated links in README and notebook

README.md

@@ -1,5 +1,3 @@
-**Status:** Active (under active development, breaking changes may occur)
-
 <img src="data/logo.jpg" width=25% align="right" /> [![Build status](https://travis-ci.org/openai/baselines.svg?branch=master)](https://travis-ci.org/openai/baselines)
 
 # Baselines

baselines/a2c/runner.py

@@ -37,6 +37,9 @@ class Runner(AbstractEnvRunner):
             obs, rewards, dones, _ = self.env.step(actions)
             self.states = states
             self.dones = dones
+            for n, done in enumerate(dones):
+                if done:
+                    self.obs[n] = self.obs[n]*0
             self.obs = obs
             mb_rewards.append(rewards)
         mb_dones.append(self.dones)

baselines/acer/acer.py

@@ -75,8 +75,8 @@ class Model(object):
         train_ob_placeholder = tf.placeholder(dtype=ob_space.dtype, shape=(nenvs*(nsteps+1),) + ob_space.shape)
         with tf.variable_scope('acer_model', reuse=tf.AUTO_REUSE):
 
-            step_model = policy(nbatch=nenvs, nsteps=1, observ_placeholder=step_ob_placeholder, sess=sess)
-            train_model = policy(nbatch=nbatch, nsteps=nsteps, observ_placeholder=train_ob_placeholder, sess=sess)
+            step_model = policy(observ_placeholder=step_ob_placeholder, sess=sess)
+            train_model = policy(observ_placeholder=train_ob_placeholder, sess=sess)
 
 
         params = find_trainable_variables("acer_model")
@@ -94,7 +94,7 @@ class Model(object):
             return v
 
         with tf.variable_scope("acer_model", custom_getter=custom_getter, reuse=True):
-            polyak_model = policy(nbatch=nbatch, nsteps=nsteps, observ_placeholder=train_ob_placeholder, sess=sess)
+            polyak_model = policy(observ_placeholder=train_ob_placeholder, sess=sess)
 
         # Notation: (var) = batch variable, (var)s = seqeuence variable, (var)_i = variable index by action at step i
 

baselines/bench/benchmarks.py

@@ -156,10 +156,9 @@ register_benchmark({
 
 # HER DDPG
 
-_fetch_tasks = ['FetchReach-v1', 'FetchPush-v1', 'FetchSlide-v1']
 register_benchmark({
-    'name': 'Fetch1M',
-    'description': 'Fetch* benchmarks for 1M timesteps',
-    'tasks': [{'trials': 6, 'env_id': env_id, 'num_timesteps': int(1e6)} for env_id in _fetch_tasks]
+    'name': 'HerDdpg',
+    'description': 'Smoke-test only benchmark of HER',
+    'tasks': [{'trials': 1, 'env_id': 'FetchReach-v1'}]
 })
 

baselines/common/atari_wrappers.py

@@ -72,8 +72,8 @@ class EpisodicLifeEnv(gym.Wrapper):
         # then update lives to handle bonus lives
         lives = self.env.unwrapped.ale.lives()
         if lives < self.lives and lives > 0:
-            # for Qbert sometimes we stay in lives == 0 condition for a few frames
-            # so it's important to keep lives > 0, so that we only reset once
+            # for Qbert sometimes we stay in lives == 0 condtion for a few frames
+            # so its important to keep lives > 0, so that we only reset once
             # the environment advertises done.
             done = True
         self.lives = lives
@@ -129,26 +129,18 @@ class ClipRewardEnv(gym.RewardWrapper):
         return np.sign(reward)
 
 class WarpFrame(gym.ObservationWrapper):
-    def __init__(self, env, width=84, height=84, grayscale=True):
+    def __init__(self, env):
         """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:
-            self.observation_space = spaces.Box(low=0, high=255,
-                shape=(self.height, self.width, 3), dtype=np.uint8)
+        self.width = 84
+        self.height = 84
+        self.observation_space = spaces.Box(low=0, high=255,
+            shape=(self.height, self.width, 1), dtype=np.uint8)
 
     def observation(self, frame):
-        if self.grayscale:
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
         frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
-        if self.grayscale:
-            frame = np.expand_dims(frame, -1)
-        return frame
+        return frame[:, :, None]
 
 class FrameStack(gym.Wrapper):
     def __init__(self, env, k):
@@ -164,7 +156,7 @@ class FrameStack(gym.Wrapper):
         self.k = k
         self.frames = deque([], maxlen=k)
         shp = env.observation_space.shape
-        self.observation_space = spaces.Box(low=0, high=255, shape=(shp[:-1] + (shp[-1] * k,)), dtype=env.observation_space.dtype)
+        self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=env.observation_space.dtype)
 
     def reset(self):
         ob = self.env.reset()
@@ -205,7 +197,7 @@ class LazyFrames(object):
 
     def _force(self):
         if self._out is None:
-            self._out = np.concatenate(self._frames, axis=-1)
+            self._out = np.concatenate(self._frames, axis=2)
             self._frames = None
         return self._out
 

baselines/common/cmd_util.py

@@ -18,50 +18,33 @@ 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
 
-def make_vec_env(env_id, env_type, num_env, seed,
-                 wrapper_kwargs=None,
-                 start_index=0,
-                 reward_scale=1.0,
-                 flatten_dict_observations=True,
-                 gamestate=None,
-                 initializer=None,
-                 env_kwargs=None,
-                 force_dummy=False):
+def make_vec_env(env_id, env_type, num_env, seed, wrapper_kwargs=None, start_index=0, reward_scale=1.0, gamestate=None):
     """
     Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
     """
-    wrapper_kwargs = wrapper_kwargs or {}
+    if wrapper_kwargs is None: wrapper_kwargs = {}
     mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
     seed = seed + 10000 * mpi_rank if seed is not None else None
-    logger_dir = logger.get_dir()
-    def make_thunk(rank, initializer=None):
+    def make_thunk(rank):
         return lambda: make_env(
             env_id=env_id,
             env_type=env_type,
-            mpi_rank=mpi_rank,
-            subrank=rank,
+            subrank = rank,
             seed=seed,
             reward_scale=reward_scale,
             gamestate=gamestate,
-            flatten_dict_observations=flatten_dict_observations,
-            wrapper_kwargs=wrapper_kwargs,
-            logger_dir=logger_dir,
-            initializer=initializer,
-            env_kwargs=env_kwargs,
+            wrapper_kwargs=wrapper_kwargs
         )
 
     set_global_seeds(seed)
-    if not force_dummy and num_env > 1:
-        return SubprocVecEnv([make_thunk(i + start_index, initializer=initializer) for i in range(num_env)])
+    if num_env > 1:
+        return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
     else:
-        return DummyVecEnv([make_thunk(i + start_index, initializer=None) for i in range(num_env)])
+        return DummyVecEnv([make_thunk(start_index)])
 
 
-def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, 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 {}
+def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate=None, wrapper_kwargs={}):
+    mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
     if env_type == 'atari':
         env = make_atari(env_id)
     elif env_type == 'retro':
@@ -69,15 +52,11 @@ 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 or {}))
-
-    if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict):
-        keys = env.observation_space.spaces.keys()
-        env = gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))
+        env = gym.make(env_id)
 
     env.seed(seed + subrank if seed is not None else None)
     env = Monitor(env,
-                  logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
+                  logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
                   allow_early_resets=True)
 
     if env_type == 'atari':
@@ -144,7 +123,6 @@ def common_arg_parser():
     """
     parser = arg_parser()
     parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
-    parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str)
     parser.add_argument('--seed', help='RNG seed', type=int, default=None)
     parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
     parser.add_argument('--num_timesteps', type=float, default=1e6),
@@ -156,7 +134,6 @@ 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

@@ -62,7 +62,7 @@ class CategoricalPdType(PdType):
     def pdclass(self):
         return CategoricalPd
     def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
-        pdparam = _matching_fc(latent_vector, 'pi', self.ncat, init_scale=init_scale, init_bias=init_bias)
+        pdparam = fc(latent_vector, 'pi', self.ncat, init_scale=init_scale, init_bias=init_bias)
         return self.pdfromflat(pdparam), pdparam
 
     def param_shape(self):
@@ -75,15 +75,14 @@ class CategoricalPdType(PdType):
 
 class MultiCategoricalPdType(PdType):
     def __init__(self, nvec):
-        self.ncats = nvec.astype('int32')
-        assert (self.ncats > 0).all()
+        self.ncats = nvec
     def pdclass(self):
         return MultiCategoricalPd
     def pdfromflat(self, flat):
         return MultiCategoricalPd(self.ncats, flat)
 
     def pdfromlatent(self, latent, init_scale=1.0, init_bias=0.0):
-        pdparam = _matching_fc(latent, 'pi', self.ncats.sum(), init_scale=init_scale, init_bias=init_bias)
+        pdparam = fc(latent, 'pi', self.ncats.sum(), init_scale=init_scale, init_bias=init_bias)
         return self.pdfromflat(pdparam), pdparam
 
     def param_shape(self):
@@ -100,7 +99,7 @@ class DiagGaussianPdType(PdType):
         return DiagGaussianPd
 
     def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
-        mean = _matching_fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
+        mean = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
         logstd = tf.get_variable(name='pi/logstd', shape=[1, self.size], initializer=tf.zeros_initializer())
         pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
         return self.pdfromflat(pdparam), mean
@@ -124,7 +123,7 @@ class BernoulliPdType(PdType):
     def sample_dtype(self):
         return tf.int32
     def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
-        pdparam = _matching_fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
+        pdparam = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
         return self.pdfromflat(pdparam), pdparam
 
 # WRONG SECOND DERIVATIVES
@@ -346,9 +345,3 @@ def validate_probtype(probtype, pdparam):
     assert np.abs(klval - klval_ll) < 3 * klval_ll_stderr # within 3 sigmas
     print('ok on', probtype, pdparam)
 
-
-def _matching_fc(tensor, name, size, init_scale, init_bias):
-    if tensor.shape[-1] == size:
-        return tensor
-    else:
-        return fc(tensor, name, size, init_scale=init_scale, init_bias=init_bias)

baselines/common/plot_util.py

@@ -168,7 +168,6 @@ def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, ve
          - monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
          - progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file
     '''
-    import re
     if isinstance(root_dir_or_dirs, str):
         rootdirs = [osp.expanduser(root_dir_or_dirs)]
     else:
@@ -180,9 +179,7 @@ def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, ve
             if '-proc' in dirname:
                 files[:] = []
                 continue
-            monitor_re = re.compile(r'(\d+\.)?(\d+\.)?monitor\.csv')
-            if set(['metadata.json', 'monitor.json', 'progress.json', 'progress.csv']).intersection(files) or \
-               any([f for f in files if monitor_re.match(f)]):  # also match monitor files like 0.1.monitor.csv
+            if set(['metadata.json', 'monitor.json', 'monitor.csv', 'progress.json', 'progress.csv']).intersection(files):
                 # used to be uncommented, which means do not go deeper than current directory if any of the data files
                 # are found
                 # dirs[:] = []

baselines/common/retro_wrappers.py

@@ -132,8 +132,10 @@ class MovieRecord(gym.Wrapper):
         self.epcount = 0
     def reset(self):
         if self.epcount % self.k == 0:
+            print('saving movie this episode', self.savedir)
             self.env.unwrapped.movie_path = self.savedir
         else:
+            print('not saving this episode')
             self.env.unwrapped.movie_path = None
             self.env.unwrapped.movie = None
         self.epcount += 1

baselines/common/tests/test_fetchreach.py

@@ -1,39 +0,0 @@
-import pytest
-import gym
-
-from baselines.run import get_learn_function
-from baselines.common.tests.util import reward_per_episode_test
-
-pytest.importorskip('mujoco_py')
-
-common_kwargs = dict(
-    network='mlp',
-    seed=0,
-)
-
-learn_kwargs = {
-    'her': dict(total_timesteps=2000)
-}
-
-@pytest.mark.slow
-@pytest.mark.parametrize("alg", learn_kwargs.keys())
-def test_fetchreach(alg):
-    '''
-    Test if the algorithm (with an mlp policy)
-    can learn the FetchReach task
-    '''
-
-    kwargs = common_kwargs.copy()
-    kwargs.update(learn_kwargs[alg])
-
-    learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
-    def env_fn():
-
-        env = gym.make('FetchReach-v1')
-        env.seed(0)
-        return env
-
-    reward_per_episode_test(env_fn, learn_fn, -15)
-
-if __name__ == '__main__':
-    test_fetchreach('her')

baselines/common/tests/test_serialization.py

@@ -103,9 +103,9 @@ def test_coexistence(learn_fn, network_fn):
     kwargs.update(learn_kwargs[learn_fn])
 
     learn =  partial(learn, env=env, network=network_fn, total_timesteps=0, **kwargs)
-    make_session(make_default=True, graph=tf.Graph())
+    make_session(make_default=True, graph=tf.Graph());
     model1 = learn(seed=1)
-    make_session(make_default=True, graph=tf.Graph())
+    make_session(make_default=True, graph=tf.Graph());
     model2 = learn(seed=2)
 
     model1.step(env.observation_space.sample())

baselines/common/tests/test_tf_util.py

@@ -18,9 +18,7 @@ def test_function():
             initialize()
 
             assert lin(2) == 6
-            assert lin(x=3) == 9
             assert lin(2, 2) == 10
-            assert lin(x=2, y=3) == 12
 
 
 def test_multikwargs():

baselines/common/tests/util.py

@@ -63,7 +63,7 @@ def rollout(env, model, n_trials):
 
     for i in range(n_trials):
         obs = env.reset()
-        state = model.initial_state if hasattr(model, 'initial_state') else None
+        state = model.initial_state
         episode_rew = []
         episode_actions = []
         episode_obs = []

baselines/common/tf_util.py

@@ -165,10 +165,6 @@ def function(inputs, outputs, updates=None, givens=None):
     outputs: [tf.Variable] or tf.Variable
         list of outputs or a single output to be returned from function. Returned
         value will also have the same shape.
-    updates: [tf.Operation] or tf.Operation
-        list of update functions or single update function that will be run whenever
-        the function is called. The return is ignored.
-
     """
     if isinstance(outputs, list):
         return _Function(inputs, outputs, updates, givens=givens)
@@ -186,7 +182,6 @@ class _Function(object):
             if not hasattr(inpt, 'make_feed_dict') and not (type(inpt) is tf.Tensor and len(inpt.op.inputs) == 0):
                 assert False, "inputs should all be placeholders, constants, or have a make_feed_dict method"
         self.inputs = inputs
-        self.input_names = {inp.name.split("/")[-1].split(":")[0]: inp for inp in inputs}
         updates = updates or []
         self.update_group = tf.group(*updates)
         self.outputs_update = list(outputs) + [self.update_group]
@@ -198,17 +193,15 @@ class _Function(object):
         else:
             feed_dict[inpt] = adjust_shape(inpt, value)
 
-    def __call__(self, *args, **kwargs):
-        assert len(args) + len(kwargs) <= len(self.inputs), "Too many arguments provided"
+    def __call__(self, *args):
+        assert len(args) <= len(self.inputs), "Too many arguments provided"
         feed_dict = {}
-        # Update feed dict with givens.
-        for inpt in self.givens:
-            feed_dict[inpt] = adjust_shape(inpt, feed_dict.get(inpt, self.givens[inpt]))
         # Update the args
         for inpt, value in zip(self.inputs, args):
             self._feed_input(feed_dict, inpt, value)
-        for inpt_name, value in kwargs.items():
-            self._feed_input(feed_dict, self.input_names[inpt_name], value)
+        # Update feed dict with givens.
+        for inpt in self.givens:
+            feed_dict[inpt] = adjust_shape(inpt, feed_dict.get(inpt, self.givens[inpt]))
         results = get_session().run(self.outputs_update, feed_dict=feed_dict)[:-1]
         return results
 
@@ -340,7 +333,7 @@ def save_state(fname, sess=None):
 
 def save_variables(save_path, variables=None, sess=None):
     sess = sess or get_session()
-    variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+    variables = variables or tf.trainable_variables()
 
     ps = sess.run(variables)
     save_dict = {v.name: value for v, value in zip(variables, ps)}
@@ -351,7 +344,7 @@ def save_variables(save_path, variables=None, sess=None):
 
 def load_variables(load_path, variables=None, sess=None):
     sess = sess or get_session()
-    variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
+    variables = variables or tf.trainable_variables()
 
     loaded_params = joblib.load(os.path.expanduser(load_path))
     restores = []

baselines/common/vec_env/dummy_vec_env.py

@@ -27,7 +27,6 @@ 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.specs = [e.spec for e in self.envs]
 
     def step_async(self, actions):
         listify = True

baselines/common/vec_env/shmem_vec_env.py

@@ -54,7 +54,6 @@ class ShmemVecEnv(VecEnv):
             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

@@ -57,7 +57,6 @@ class SubprocVecEnv(VecEnv):
         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):
@@ -71,13 +70,13 @@ class SubprocVecEnv(VecEnv):
         results = [remote.recv() for remote in self.remotes]
         self.waiting = False
         obs, rews, dones, infos = zip(*results)
-        return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos
+        return np.stack(obs), np.stack(rews), np.stack(dones), infos
 
     def reset(self):
         self._assert_not_closed()
         for remote in self.remotes:
             remote.send(('reset', None))
-        return _flatten_obs([remote.recv() for remote in self.remotes])
+        return np.stack([remote.recv() for remote in self.remotes])
 
     def close_extras(self):
         self.closed = True
@@ -98,17 +97,3 @@ class SubprocVecEnv(VecEnv):
 
     def _assert_not_closed(self):
         assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"
-
-
-def _flatten_obs(obs):
-    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/ddpg/ddpg_learner.py

@@ -67,6 +67,7 @@ class DDPG(object):
     def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None,
         gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True,
         batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf),
+        adaptive_param_noise=True, adaptive_param_noise_policy_threshold=.1,
         critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.):
         # Inputs.
         self.obs0 = tf.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0')
@@ -185,7 +186,7 @@ class DDPG(object):
         normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1])
         self.critic_loss = tf.reduce_mean(tf.square(self.normalized_critic_tf - normalized_critic_target_tf))
         if self.critic_l2_reg > 0.:
-            critic_reg_vars = [var for var in self.critic.trainable_vars if var.name.endswith('/w:0') and 'output' not in var.name]
+            critic_reg_vars = [var for var in self.critic.trainable_vars if 'kernel' in var.name and 'output' not in var.name]
             for var in critic_reg_vars:
                 logger.info('  regularizing: {}'.format(var.name))
             logger.info('  applying l2 regularization with {}'.format(self.critic_l2_reg))

baselines/ddpg/models.py

@@ -42,7 +42,7 @@ class Critic(Model):
         with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
             x = tf.concat([obs, action], axis=-1) # this assumes observation and action can be concatenated
             x = self.network_builder(x)
-            x = tf.layers.dense(x, 1, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3), name='output')
+            x = tf.layers.dense(x, 1, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
         return x
 
     @property

baselines/ddpg/test_smoke.py

@@ -1,17 +0,0 @@
-from baselines.run import main as M
-
-def _run(argstr):
-    M(('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr).split(' '))
-
-def test_popart():
-    _run('--normalize_returns=True --popart=True')
-
-def test_noise_normal():
-    _run('--noise_type=normal_0.1')
-
-def test_noise_ou():
-    _run('--noise_type=ou_0.1')
-
-def test_noise_adaptive():
-    _run('--noise_type=adaptive-param_0.2,normal_0.1')
-

baselines/deepq/__init__.py

@@ -5,4 +5,4 @@ from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer
 
 def wrap_atari_dqn(env):
     from baselines.common.atari_wrappers import wrap_deepmind
-    return wrap_deepmind(env, frame_stack=True, scale=False)
+    return wrap_deepmind(env, frame_stack=True, scale=True)

baselines/deepq/build_graph.py

@@ -33,7 +33,7 @@ The functions in this file can are used to create the following functions:
     stochastic: bool
         if set to False all the actions are always deterministic (default False)
     update_eps_ph: float
-        update epsilon to a new value, if negative no update happens
+        update epsilon a new value, if negative not update happens
         (default: no update)
     reset_ph: bool
         reset the perturbed policy by sampling a new perturbation

baselines/deepq/models.py

@@ -2,9 +2,9 @@ import tensorflow as tf
 import tensorflow.contrib.layers as layers
 
 
-def _mlp(hiddens, input_, num_actions, scope, reuse=False, layer_norm=False):
+def _mlp(hiddens, inpt, num_actions, scope, reuse=False, layer_norm=False):
     with tf.variable_scope(scope, reuse=reuse):
-        out = input_
+        out = inpt
         for hidden in hiddens:
             out = layers.fully_connected(out, num_outputs=hidden, activation_fn=None)
             if layer_norm:
@@ -21,9 +21,6 @@ def mlp(hiddens=[], layer_norm=False):
     ----------
     hiddens: [int]
         list of sizes of hidden layers
-    layer_norm: bool
-        if true applies layer normalization for every layer
-        as described in https://arxiv.org/abs/1607.06450
 
     Returns
     -------
@@ -33,9 +30,9 @@ def mlp(hiddens=[], layer_norm=False):
     return lambda *args, **kwargs: _mlp(hiddens, layer_norm=layer_norm, *args, **kwargs)
 
 
-def _cnn_to_mlp(convs, hiddens, dueling, input_, num_actions, scope, reuse=False, layer_norm=False):
+def _cnn_to_mlp(convs, hiddens, dueling, inpt, num_actions, scope, reuse=False, layer_norm=False):
     with tf.variable_scope(scope, reuse=reuse):
-        out = input_
+        out = inpt
         with tf.variable_scope("convnet"):
             for num_outputs, kernel_size, stride in convs:
                 out = layers.convolution2d(out,
@@ -75,7 +72,7 @@ def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
 
     Parameters
     ----------
-    convs: [(int, int, int)]
+    convs: [(int, int int)]
         list of convolutional layers in form of
         (num_outputs, kernel_size, stride)
     hiddens: [int]
@@ -83,9 +80,6 @@ def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
     dueling: bool
         if true double the output MLP to compute a baseline
         for action scores
-    layer_norm: bool
-        if true applies layer normalization for every layer
-        as described in https://arxiv.org/abs/1607.06450
 
     Returns
     -------

baselines/gail/result/gail-result.md

@@ -24,7 +24,7 @@ Hopper-v1, Walker2d-v1, HalfCheetah-v1, Humanoid-v1, HumanoidStandup-v1. Every i
 
 For details (e.g., adversarial loss, discriminator accuracy, etc.) about GAIL training, please see [here](https://drive.google.com/drive/folders/1nnU8dqAV9i37-_5_vWIspyFUJFQLCsDD?usp=sharing)
 
-### Determinstic Policy (Set std=0)
+### Determinstic Polciy (Set std=0)
 |   | Un-normalized | Normalized |
 |---|---|---|
 | Hopper-v1 | <img src='Hopper-unnormalized-deterministic-scores.png'> | <img src='Hopper-normalized-deterministic-scores.png'> |

baselines/her/README.md

@@ -6,29 +6,26 @@ For details on Hindsight Experience Replay (HER), please read the [paper](https:
 ### Getting started
 Training an agent is very simple:
 ```bash
-python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000
+python -m baselines.her.experiment.train
 ```
 This will train a DDPG+HER agent on the `FetchReach` environment.
 You should see the success rate go up quickly to `1.0`, which means that the agent achieves the
-desired goal in 100% of the cases (note how HER can solve it in <5k steps - try doing that with PPO by replacing her with ppo2 :))
-The training script logs other diagnostics as well. Policy at the end of the training can be saved using `--save_path` flag, for instance:
-```bash
-python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --save_path=~/policies/her/fetchreach5k
-```
+desired goal in 100% of the cases.
+The training script logs other diagnostics as well and pickles the best policy so far (w.r.t. to its test success rate),
+the latest policy, and, if enabled, a history of policies every K epochs.
 
-To inspect what the agent has learned, use the `--play` flag: 
+To inspect what the agent has learned, use the play script:
 ```bash
-python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --play
+python -m baselines.her.experiment.play /path/to/an/experiment/policy_best.pkl
 ```
-(note `--play` can be combined with `--load_path`, which lets one load trained policies, for more results see [README.md](../../README.md))
+You can try it right now with the results of the training step (the script prints out the path for you).
+This should visualize the current policy for 10 episodes and will also print statistics.
 
 
 ### Reproducing results
-In [Plappert et al. (2018)](https://arxiv.org/abs/1802.09464), 38 trajectories were generated in parallel
-(19 MPI processes, each generating computing gradients from 2 trajectories and aggregating). 
-To reproduce that behaviour, use 
+In order to reproduce the results from [Plappert et al. (2018)](https://arxiv.org/abs/1802.09464), run the following command:
 ```bash
-mpirun -np 19 python -m baselines.run --num_env=2 --alg=her ... 
+python -m baselines.her.experiment.train --num_cpu 19
 ```
 This will require a machine with sufficient amount of physical CPU cores. In our experiments,
 we used [Azure's D15v2 instances](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/sizes),
@@ -48,13 +45,6 @@ python experiment/data_generation/fetch_data_generation.py
 ```
 This outputs ```data_fetch_random_100.npz``` file which is our data file.
 
-To launch training with demonstrations (more technically, with behaviour cloning loss as an auxilliary loss), run the following
-```bash
-python -m baselines.run --alg=her --env=FetchPickAndPlace-v1 --num_timesteps=2.5e6 --demo_file=/Path/to/demo_file.npz
-```
-This will train a DDPG+HER agent on the `FetchPickAndPlace` environment by using previously generated demonstration data.
-To inspect what the agent has learned, use the `--play` flag as described above.
-
 #### Configuration
 The provided configuration is for training an agent with HER without demonstrations, we need to change a few paramters for the HER algorithm to learn through demonstrations, to do that, set:
 
@@ -72,7 +62,13 @@ Apart from these changes the reported results also have the following configurat
 * random_eps: 0.1  - percentage of time a random action is taken
 * noise_eps: 0.1  - std of gaussian noise added to not-completely-random actions
 
-These parameters can be changed either in [experiment/config.py](experiment/config.py) or passed to the command line as `--param=value`)
+Now training an agent with pre-recorded demonstrations:
+```bash
+python -m baselines.her.experiment.train --env=FetchPickAndPlace-v0 --n_epochs=1000 --demo_file=/Path/to/demo_file.npz --num_cpu=1
+```
+
+This will train a DDPG+HER agent on the `FetchPickAndPlace` environment by using previously generated demonstration data.
+To inspect what the agent has learned, use the play script as described above.
 
 ### Results
 Training with demonstrations helps overcome the exploration problem and achieves a faster and better convergence. The following graphs contrast the difference between training with and without demonstration data, We report the mean Q values vs Epoch and the Success Rate vs Epoch:
@@ -82,4 +78,3 @@ Training with demonstrations helps overcome the exploration problem and achieves
 <center><img src="../../data/fetchPickAndPlaceContrast.png"></center>
 <div class="thecap" align="middle"><b>Training results for Fetch Pick and Place task constrasting between training with and without demonstration data.</b></div>
 </div>
-

baselines/her/ddpg.py

@@ -10,14 +10,13 @@ from baselines.her.util import (
 from baselines.her.normalizer import Normalizer
 from baselines.her.replay_buffer import ReplayBuffer
 from baselines.common.mpi_adam import MpiAdam
-from baselines.common import tf_util
 
 
 def dims_to_shapes(input_dims):
     return {key: tuple([val]) if val > 0 else tuple() for key, val in input_dims.items()}
 
 
-global DEMO_BUFFER #buffer for demonstrations
+global demoBuffer #buffer for demonstrations
 
 class DDPG(object):
     @store_args
@@ -95,16 +94,16 @@ class DDPG(object):
             self._create_network(reuse=reuse)
 
         # Configure the replay buffer.
-        buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key])
+        buffer_shapes = {key: (self.T if key != 'o' else self.T+1, *input_shapes[key])
                          for key, val in input_shapes.items()}
         buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg)
-        buffer_shapes['ag'] = (self.T, self.dimg)
+        buffer_shapes['ag'] = (self.T+1, self.dimg)
 
         buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size
         self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions)
 
-        global DEMO_BUFFER
-        DEMO_BUFFER = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer
+        global demoBuffer
+        demoBuffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer
 
     def _random_action(self, n):
         return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu))
@@ -120,11 +119,6 @@ class DDPG(object):
         g = np.clip(g, -self.clip_obs, self.clip_obs)
         return o, g
 
-    def step(self, obs):
-        actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal'])
-        return actions, None, None, None
-
-
     def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False,
                     compute_Q=False):
         o, g = self._preprocess_og(o, ag, g)
@@ -157,30 +151,25 @@ class DDPG(object):
         else:
             return ret
 
-    def init_demo_buffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer
+    def initDemoBuffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer
 
         demoData = np.load(demoDataFile) #load the demonstration data from data file
         info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')]
-        info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
-
-        demo_data_obs = demoData['obs']
-        demo_data_acs = demoData['acs']
-        demo_data_info = demoData['info']
+        info_values = [np.empty((self.T, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys]
 
         for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training
             obs, acts, goals, achieved_goals = [], [] ,[] ,[]
             i = 0
-            for transition in range(self.T - 1):
-                obs.append([demo_data_obs[epsd][transition].get('observation')])
-                acts.append([demo_data_acs[epsd][transition]])
-                goals.append([demo_data_obs[epsd][transition].get('desired_goal')])
-                achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')])
+            for transition in range(self.T):
+                obs.append([demoData['obs'][epsd ][transition].get('observation')])
+                acts.append([demoData['acs'][epsd][transition]])
+                goals.append([demoData['obs'][epsd][transition].get('desired_goal')])
+                achieved_goals.append([demoData['obs'][epsd][transition].get('achieved_goal')])
                 for idx, key in enumerate(info_keys):
-                    info_values[idx][transition, i] = demo_data_info[epsd][transition][key]
+                    info_values[idx][transition, i] = demoData['info'][epsd][transition][key]
 
-
-            obs.append([demo_data_obs[epsd][self.T - 1].get('observation')])
-            achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')])
+            obs.append([demoData['obs'][epsd][self.T].get('observation')])
+            achieved_goals.append([demoData['obs'][epsd][self.T].get('achieved_goal')])
 
             episode = dict(o=obs,
                            u=acts,
@@ -190,9 +179,10 @@ class DDPG(object):
                 episode['info_{}'.format(key)] = value
 
             episode = convert_episode_to_batch_major(episode)
-            global DEMO_BUFFER
-            DEMO_BUFFER.store_episode(episode) # create the observation dict and append them into the demonstration buffer
-            logger.debug("Demo buffer size currently ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
+            global demoBuffer
+            demoBuffer.store_episode(episode) # create the observation dict and append them into the demonstration buffer
+
+            print("Demo buffer size currently ", demoBuffer.get_current_size()) #print out the demonstration buffer size
 
             if update_stats:
                 # add transitions to normalizer to normalize the demo data as well
@@ -201,7 +191,7 @@ class DDPG(object):
                 num_normalizing_transitions = transitions_in_episode_batch(episode)
                 transitions = self.sample_transitions(episode, num_normalizing_transitions)
 
-                o, g, ag = transitions['o'], transitions['g'], transitions['ag']
+                o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag']
                 transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
                 # No need to preprocess the o_2 and g_2 since this is only used for stats
 
@@ -212,8 +202,6 @@ class DDPG(object):
                 self.g_stats.recompute_stats()
             episode.clear()
 
-        logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size
-
     def store_episode(self, episode_batch, update_stats=True):
         """
         episode_batch: array of batch_size x (T or T+1) x dim_key
@@ -229,7 +217,7 @@ class DDPG(object):
             num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
             transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
 
-            o, g, ag = transitions['o'], transitions['g'], transitions['ag']
+            o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag']
             transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
             # No need to preprocess the o_2 and g_2 since this is only used for stats
 
@@ -263,9 +251,9 @@ class DDPG(object):
     def sample_batch(self):
         if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE
             transitions = self.buffer.sample(self.batch_size - self.demo_batch_size)
-            global DEMO_BUFFER
-            transitions_demo = DEMO_BUFFER.sample(self.demo_batch_size) #sample from the demo buffer
-            for k, values in transitions_demo.items():
+            global demoBuffer
+            transitionsDemo = demoBuffer.sample(self.demo_batch_size) #sample from the demo buffer
+            for k, values in transitionsDemo.items():
                 rolloutV = transitions[k].tolist()
                 for v in values:
                     rolloutV.append(v.tolist())
@@ -314,7 +302,10 @@ class DDPG(object):
 
     def _create_network(self, reuse=False):
         logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u))
-        self.sess = tf_util.get_session()
+
+        self.sess = tf.get_default_session()
+        if self.sess is None:
+            self.sess = tf.InteractiveSession()
 
         # running averages
         with tf.variable_scope('o_stats') as vs:
@@ -376,6 +367,8 @@ class DDPG(object):
             self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
             self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
 
+        self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf)
+        self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u))
         Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q'))
         pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi'))
         assert len(self._vars('main/Q')) == len(Q_grads_tf)
@@ -410,7 +403,7 @@ class DDPG(object):
         logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))]
         logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))]
 
-        if prefix != '' and not prefix.endswith('/'):
+        if prefix is not '' and not prefix.endswith('/'):
             return [(prefix + '/' + key, val) for key, val in logs]
         else:
             return logs
@@ -442,7 +435,3 @@ class DDPG(object):
         assert(len(vars) == len(state["tf"]))
         node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])]
         self.sess.run(node)
-
-    def save(self, save_path):
-        tf_util.save_variables(save_path)
-

baselines/her/experiment/config.py

@@ -1,11 +1,10 @@
-import os
 import numpy as np
 import gym
 
 from baselines import logger
 from baselines.her.ddpg import DDPG
-from baselines.her.her_sampler import make_sample_her_transitions
-from baselines.bench.monitor import Monitor
+from baselines.her.her import make_sample_her_transitions
+
 
 DEFAULT_ENV_PARAMS = {
     'FetchReach-v1': {
@@ -73,32 +72,16 @@ def cached_make_env(make_env):
 def prepare_params(kwargs):
     # DDPG params
     ddpg_params = dict()
+
     env_name = kwargs['env_name']
 
-    def make_env(subrank=None):
-        env = gym.make(env_name)
-        if subrank is not None and logger.get_dir() is not None:
-            try:
-                from mpi4py import MPI
-                mpi_rank = MPI.COMM_WORLD.Get_rank()
-            except ImportError:
-                MPI = None
-                mpi_rank = 0
-                logger.warn('Running with a single MPI process. This should work, but the results may differ from the ones publshed in Plappert et al.')
-
-            max_episode_steps = env._max_episode_steps
-            env =  Monitor(env,
-                           os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
-                           allow_early_resets=True)
-            # hack to re-expose _max_episode_steps (ideally should replace reliance on it downstream)
-            env = gym.wrappers.TimeLimit(env, max_episode_steps=max_episode_steps)
-        return env
-
+    def make_env():
+        return gym.make(env_name)
     kwargs['make_env'] = make_env
     tmp_env = cached_make_env(kwargs['make_env'])
     assert hasattr(tmp_env, '_max_episode_steps')
     kwargs['T'] = tmp_env._max_episode_steps
-
+    tmp_env.reset()
     kwargs['max_u'] = np.array(kwargs['max_u']) if isinstance(kwargs['max_u'], list) else kwargs['max_u']
     kwargs['gamma'] = 1. - 1. / kwargs['T']
     if 'lr' in kwargs:

baselines/her/experiment/data_generation/fetch_data_generation.py

@@ -1,5 +1,18 @@
 import gym
+import time
+import random
 import numpy as np
+import rospy
+import roslaunch
+
+from random import randint
+from std_srvs.srv import Empty
+from sensor_msgs.msg import JointState
+from geometry_msgs.msg import PoseStamped
+from geometry_msgs.msg import Pose
+from std_msgs.msg import Float64
+from controller_manager_msgs.srv import SwitchController
+from gym.utils import seeding
 
 
 """Data generation for the case of a single block pick and place in Fetch Env"""
@@ -9,7 +22,7 @@ observations = []
 infos = []
 
 def main():
-    env = gym.make('FetchPickAndPlace-v1')
+    env = gym.make('FetchPickAndPlace-v0')
     numItr = 100
     initStateSpace = "random"
     env.reset()
@@ -18,19 +31,21 @@ def main():
         obs = env.reset()
         print("ITERATION NUMBER ", len(actions))
         goToGoal(env, obs)
-
+        
 
     fileName = "data_fetch"
     fileName += "_" + initStateSpace
     fileName += "_" + str(numItr)
     fileName += ".npz"
-
+    
     np.savez_compressed(fileName, acs=actions, obs=observations, info=infos) # save the file
 
 def goToGoal(env, lastObs):
 
     goal = lastObs['desired_goal']
     objectPos = lastObs['observation'][3:6]
+    gripperPos = lastObs['observation'][:3]
+    gripperState = lastObs['observation'][9:11]
     object_rel_pos = lastObs['observation'][6:9]
     episodeAcs = []
     episodeObs = []
@@ -38,7 +53,7 @@ def goToGoal(env, lastObs):
 
     object_oriented_goal = object_rel_pos.copy()
     object_oriented_goal[2] += 0.03 # first make the gripper go slightly above the object
-
+    
     timeStep = 0 #count the total number of timesteps
     episodeObs.append(lastObs)
 
@@ -61,6 +76,8 @@ def goToGoal(env, lastObs):
         episodeObs.append(obsDataNew)
 
         objectPos = obsDataNew['observation'][3:6]
+        gripperPos = obsDataNew['observation'][:3]
+        gripperState = obsDataNew['observation'][9:11]
         object_rel_pos = obsDataNew['observation'][6:9]
 
     while np.linalg.norm(object_rel_pos) >= 0.005 and timeStep <= env._max_episode_steps :
@@ -79,6 +96,8 @@ def goToGoal(env, lastObs):
         episodeObs.append(obsDataNew)
 
         objectPos = obsDataNew['observation'][3:6]
+        gripperPos = obsDataNew['observation'][:3]
+        gripperState = obsDataNew['observation'][9:11]
         object_rel_pos = obsDataNew['observation'][6:9]
 
 
@@ -98,6 +117,8 @@ def goToGoal(env, lastObs):
         episodeObs.append(obsDataNew)
 
         objectPos = obsDataNew['observation'][3:6]
+        gripperPos = obsDataNew['observation'][:3]
+        gripperState = obsDataNew['observation'][9:11]
         object_rel_pos = obsDataNew['observation'][6:9]
 
     while True: #limit the number of timesteps in the episode to a fixed duration
@@ -113,6 +134,8 @@ def goToGoal(env, lastObs):
         episodeObs.append(obsDataNew)
 
         objectPos = obsDataNew['observation'][3:6]
+        gripperPos = obsDataNew['observation'][:3]
+        gripperState = obsDataNew['observation'][9:11]
         object_rel_pos = obsDataNew['observation'][6:9]
 
         if timeStep >= env._max_episode_steps: break

baselines/her/experiment/play.py

@@ -1,4 +1,3 @@
-# DEPRECATED, use --play flag to baselines.run instead
 import click
 import numpy as np
 import pickle

baselines/her/experiment/plot.py

@@ -1,5 +1,3 @@
-# DEPRECATED, use baselines.common.plot_util instead
-
 import os
 import matplotlib.pyplot as plt
 import numpy as np

baselines/her/experiment/train.py

@@ -0,0 +1,194 @@
+import os
+import sys
+
+import click
+import numpy as np
+import json
+from mpi4py import MPI
+
+from baselines import logger
+from baselines.common import set_global_seeds
+from baselines.common.mpi_moments import mpi_moments
+import baselines.her.experiment.config as config
+from baselines.her.rollout import RolloutWorker
+from baselines.her.util import mpi_fork
+
+from subprocess import CalledProcessError
+
+
+def mpi_average(value):
+    if value == []:
+        value = [0.]
+    if not isinstance(value, list):
+        value = [value]
+    return mpi_moments(np.array(value))[0]
+
+
+def train(policy, rollout_worker, evaluator,
+          n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval,
+          save_policies, demo_file, **kwargs):
+    rank = MPI.COMM_WORLD.Get_rank()
+
+    latest_policy_path = os.path.join(logger.get_dir(), 'policy_latest.pkl')
+    best_policy_path = os.path.join(logger.get_dir(), 'policy_best.pkl')
+    periodic_policy_path = os.path.join(logger.get_dir(), 'policy_{}.pkl')
+
+    logger.info("Training...")
+    best_success_rate = -1
+
+    if policy.bc_loss == 1: policy.initDemoBuffer(demo_file) #initialize demo buffer if training with demonstrations
+    for epoch in range(n_epochs):
+        # train
+        rollout_worker.clear_history()
+        for _ in range(n_cycles):
+            episode = rollout_worker.generate_rollouts()
+            policy.store_episode(episode)
+            for _ in range(n_batches):
+                policy.train()
+            policy.update_target_net()
+
+        # test
+        evaluator.clear_history()
+        for _ in range(n_test_rollouts):
+            evaluator.generate_rollouts()
+
+        # record logs
+        logger.record_tabular('epoch', epoch)
+        for key, val in evaluator.logs('test'):
+            logger.record_tabular(key, mpi_average(val))
+        for key, val in rollout_worker.logs('train'):
+            logger.record_tabular(key, mpi_average(val))
+        for key, val in policy.logs():
+            logger.record_tabular(key, mpi_average(val))
+
+        if rank == 0:
+            logger.dump_tabular()
+
+        # save the policy if it's better than the previous ones
+        success_rate = mpi_average(evaluator.current_success_rate())
+        if rank == 0 and success_rate >= best_success_rate and save_policies:
+            best_success_rate = success_rate
+            logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path))
+            evaluator.save_policy(best_policy_path)
+            evaluator.save_policy(latest_policy_path)
+        if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_policies:
+            policy_path = periodic_policy_path.format(epoch)
+            logger.info('Saving periodic policy to {} ...'.format(policy_path))
+            evaluator.save_policy(policy_path)
+
+        # make sure that different threads have different seeds
+        local_uniform = np.random.uniform(size=(1,))
+        root_uniform = local_uniform.copy()
+        MPI.COMM_WORLD.Bcast(root_uniform, root=0)
+        if rank != 0:
+            assert local_uniform[0] != root_uniform[0]
+
+
+def launch(
+    env, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return,
+    demo_file, override_params={}, save_policies=True
+):
+    # Fork for multi-CPU MPI implementation.
+    if num_cpu > 1:
+        try:
+            whoami = mpi_fork(num_cpu, ['--bind-to', 'core'])
+        except CalledProcessError:
+            # fancy version of mpi call failed, try simple version
+            whoami = mpi_fork(num_cpu)
+
+        if whoami == 'parent':
+            sys.exit(0)
+        import baselines.common.tf_util as U
+        U.single_threaded_session().__enter__()
+    rank = MPI.COMM_WORLD.Get_rank()
+
+    # Configure logging
+    if rank == 0:
+        if logdir or logger.get_dir() is None:
+            logger.configure(dir=logdir)
+    else:
+        logger.configure()
+    logdir = logger.get_dir()
+    assert logdir is not None
+    os.makedirs(logdir, exist_ok=True)
+
+    # Seed everything.
+    rank_seed = seed + 1000000 * rank
+    set_global_seeds(rank_seed)
+
+    # Prepare params.
+    params = config.DEFAULT_PARAMS
+    params['env_name'] = env
+    params['replay_strategy'] = replay_strategy
+    if env in config.DEFAULT_ENV_PARAMS:
+        params.update(config.DEFAULT_ENV_PARAMS[env])  # merge env-specific parameters in
+    params.update(**override_params)  # makes it possible to override any parameter
+    with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
+        json.dump(params, f)
+    params = config.prepare_params(params)
+    config.log_params(params, logger=logger)
+
+    if num_cpu == 1:
+        logger.warn()
+        logger.warn('*** Warning ***')
+        logger.warn(
+            'You are running HER with just a single MPI worker. This will work, but the ' +
+            'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) ' +
+            'were obtained with --num_cpu 19. This makes a significant difference and if you ' +
+            'are looking to reproduce those results, be aware of this. Please also refer to ' +
+            'https://github.com/openai/baselines/issues/314 for further details.')
+        logger.warn('****************')
+        logger.warn()
+
+    dims = config.configure_dims(params)
+    policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return)
+
+    rollout_params = {
+        'exploit': False,
+        'use_target_net': False,
+        'use_demo_states': True,
+        'compute_Q': False,
+        'T': params['T'],
+    }
+
+    eval_params = {
+        'exploit': True,
+        'use_target_net': params['test_with_polyak'],
+        'use_demo_states': False,
+        'compute_Q': True,
+        'T': params['T'],
+    }
+
+    for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
+        rollout_params[name] = params[name]
+        eval_params[name] = params[name]
+
+    rollout_worker = RolloutWorker(params['make_env'], policy, dims, logger, **rollout_params)
+    rollout_worker.seed(rank_seed)
+
+    evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params)
+    evaluator.seed(rank_seed)
+
+    train(
+        logdir=logdir, policy=policy, rollout_worker=rollout_worker,
+        evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'],
+        n_cycles=params['n_cycles'], n_batches=params['n_batches'],
+        policy_save_interval=policy_save_interval, save_policies=save_policies, demo_file=demo_file)
+
+
+@click.command()
+@click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on')
+@click.option('--logdir', type=str, default=None, help='the path to where logs and policy pickles should go. If not specified, creates a folder in /tmp/')
+@click.option('--n_epochs', type=int, default=50, help='the number of training epochs to run')
+@click.option('--num_cpu', type=int, default=1, help='the number of CPU cores to use (using MPI)')
+@click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code')
+@click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.')
+@click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.')
+@click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped')
+@click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path')
+def main(**kwargs):
+    launch(**kwargs)
+
+
+if __name__ == '__main__':
+    main()

baselines/her/her.py

@@ -1,193 +1,63 @@
-import os
-
-import click
 import numpy as np
-import json
-from mpi4py import MPI
-
-from baselines import logger
-from baselines.common import set_global_seeds, tf_util
-from baselines.common.mpi_moments import mpi_moments
-import baselines.her.experiment.config as config
-from baselines.her.rollout import RolloutWorker
-
-def mpi_average(value):
-    if not isinstance(value, list):
-        value = [value]
-    if not any(value):
-        value = [0.]
-    return mpi_moments(np.array(value))[0]
 
 
-def train(*, policy, rollout_worker, evaluator,
-          n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval,
-          save_path, demo_file, **kwargs):
-    rank = MPI.COMM_WORLD.Get_rank()
+def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
+    """Creates a sample function that can be used for HER experience replay.
 
-    if save_path:
-        latest_policy_path = os.path.join(save_path, 'policy_latest.pkl')
-        best_policy_path = os.path.join(save_path, 'policy_best.pkl')
-        periodic_policy_path = os.path.join(save_path, 'policy_{}.pkl')
+    Args:
+        replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
+            regular DDPG experience replay is used
+        replay_k (int): the ratio between HER replays and regular replays (e.g. k = 4 -> 4 times
+            as many HER replays as regular replays are used)
+        reward_fun (function): function to re-compute the reward with substituted goals
+    """
+    if replay_strategy == 'future':
+        future_p = 1 - (1. / (1 + replay_k))
+    else:  # 'replay_strategy' == 'none'
+        future_p = 0
 
-    logger.info("Training...")
-    best_success_rate = -1
+    def _sample_her_transitions(episode_batch, batch_size_in_transitions):
+        """episode_batch is {key: array(buffer_size x T x dim_key)}
+        """
+        T = episode_batch['u'].shape[1]
+        rollout_batch_size = episode_batch['u'].shape[0]
+        batch_size = batch_size_in_transitions
 
-    if policy.bc_loss == 1: policy.init_demo_buffer(demo_file) #initialize demo buffer if training with demonstrations
+        # Select which episodes and time steps to use.
+        episode_idxs = np.random.randint(0, rollout_batch_size, batch_size)
+        t_samples = np.random.randint(T, size=batch_size)
+        transitions = {key: episode_batch[key][episode_idxs, t_samples].copy()
+                       for key in episode_batch.keys()}
 
-    # num_timesteps = n_epochs * n_cycles * rollout_length * number of rollout workers
-    for epoch in range(n_epochs):
-        # train
-        rollout_worker.clear_history()
-        for _ in range(n_cycles):
-            episode = rollout_worker.generate_rollouts()
-            policy.store_episode(episode)
-            for _ in range(n_batches):
-                policy.train()
-            policy.update_target_net()
+        # Select future time indexes proportional with probability future_p. These
+        # will be used for HER replay by substituting in future goals.
+        her_indexes = np.where(np.random.uniform(size=batch_size) < future_p)
+        future_offset = np.random.uniform(size=batch_size) * (T - t_samples)
+        future_offset = future_offset.astype(int)
+        future_t = (t_samples + 1 + future_offset)[her_indexes]
 
-        # test
-        evaluator.clear_history()
-        for _ in range(n_test_rollouts):
-            evaluator.generate_rollouts()
+        # Replace goal with achieved goal but only for the previously-selected
+        # HER transitions (as defined by her_indexes). For the other transitions,
+        # keep the original goal.
+        future_ag = episode_batch['ag'][episode_idxs[her_indexes], future_t]
+        transitions['g'][her_indexes] = future_ag
 
-        # record logs
-        logger.record_tabular('epoch', epoch)
-        for key, val in evaluator.logs('test'):
-            logger.record_tabular(key, mpi_average(val))
-        for key, val in rollout_worker.logs('train'):
-            logger.record_tabular(key, mpi_average(val))
-        for key, val in policy.logs():
-            logger.record_tabular(key, mpi_average(val))
+        # Reconstruct info dictionary for reward  computation.
+        info = {}
+        for key, value in transitions.items():
+            if key.startswith('info_'):
+                info[key.replace('info_', '')] = value
 
-        if rank == 0:
-            logger.dump_tabular()
+        # Re-compute reward since we may have substituted the goal.
+        reward_params = {k: transitions[k] for k in ['ag_2', 'g']}
+        reward_params['info'] = info
+        transitions['r'] = reward_fun(**reward_params)
 
-        # save the policy if it's better than the previous ones
-        success_rate = mpi_average(evaluator.current_success_rate())
-        if rank == 0 and success_rate >= best_success_rate and save_path:
-            best_success_rate = success_rate
-            logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path))
-            evaluator.save_policy(best_policy_path)
-            evaluator.save_policy(latest_policy_path)
-        if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_path:
-            policy_path = periodic_policy_path.format(epoch)
-            logger.info('Saving periodic policy to {} ...'.format(policy_path))
-            evaluator.save_policy(policy_path)
+        transitions = {k: transitions[k].reshape(batch_size, *transitions[k].shape[1:])
+                       for k in transitions.keys()}
 
-        # make sure that different threads have different seeds
-        local_uniform = np.random.uniform(size=(1,))
-        root_uniform = local_uniform.copy()
-        MPI.COMM_WORLD.Bcast(root_uniform, root=0)
-        if rank != 0:
-            assert local_uniform[0] != root_uniform[0]
+        assert(transitions['u'].shape[0] == batch_size_in_transitions)
 
-    return policy
+        return transitions
 
-
-def learn(*, network, env, total_timesteps,
-    seed=None,
-    eval_env=None,
-    replay_strategy='future',
-    policy_save_interval=5,
-    clip_return=True,
-    demo_file=None,
-    override_params=None,
-    load_path=None,
-    save_path=None,
-    **kwargs
-):
-
-    override_params = override_params or {}
-    if MPI is not None:
-        rank = MPI.COMM_WORLD.Get_rank()
-        num_cpu = MPI.COMM_WORLD.Get_size()
-
-    # Seed everything.
-    rank_seed = seed + 1000000 * rank if seed is not None else None
-    set_global_seeds(rank_seed)
-
-    # Prepare params.
-    params = config.DEFAULT_PARAMS
-    env_name = env.specs[0].id
-    params['env_name'] = env_name
-    params['replay_strategy'] = replay_strategy
-    if env_name in config.DEFAULT_ENV_PARAMS:
-        params.update(config.DEFAULT_ENV_PARAMS[env_name])  # merge env-specific parameters in
-    params.update(**override_params)  # makes it possible to override any parameter
-    with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
-         json.dump(params, f)
-    params = config.prepare_params(params)
-    params['rollout_batch_size'] = env.num_envs
-
-    if demo_file is not None:
-        params['bc_loss'] = 1
-    params.update(kwargs)
-
-    config.log_params(params, logger=logger)
-
-    if num_cpu == 1:
-        logger.warn()
-        logger.warn('*** Warning ***')
-        logger.warn(
-            'You are running HER with just a single MPI worker. This will work, but the ' +
-            'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) ' +
-            'were obtained with --num_cpu 19. This makes a significant difference and if you ' +
-            'are looking to reproduce those results, be aware of this. Please also refer to ' +
-            'https://github.com/openai/baselines/issues/314 for further details.')
-        logger.warn('****************')
-        logger.warn()
-
-    dims = config.configure_dims(params)
-    policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return)
-    if load_path is not None:
-        tf_util.load_variables(load_path)
-
-    rollout_params = {
-        'exploit': False,
-        'use_target_net': False,
-        'use_demo_states': True,
-        'compute_Q': False,
-        'T': params['T'],
-    }
-
-    eval_params = {
-        'exploit': True,
-        'use_target_net': params['test_with_polyak'],
-        'use_demo_states': False,
-        'compute_Q': True,
-        'T': params['T'],
-    }
-
-    for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']:
-        rollout_params[name] = params[name]
-        eval_params[name] = params[name]
-
-    eval_env = eval_env or env
-
-    rollout_worker = RolloutWorker(env, policy, dims, logger, monitor=True, **rollout_params)
-    evaluator = RolloutWorker(eval_env, policy, dims, logger, **eval_params)
-
-    n_cycles = params['n_cycles']
-    n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size
-
-    return train(
-        save_path=save_path, policy=policy, rollout_worker=rollout_worker,
-        evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'],
-        n_cycles=params['n_cycles'], n_batches=params['n_batches'],
-        policy_save_interval=policy_save_interval, demo_file=demo_file)
-
-
-@click.command()
-@click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on')
-@click.option('--total_timesteps', type=int, default=int(5e5), help='the number of timesteps to run')
-@click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code')
-@click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.')
-@click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.')
-@click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped')
-@click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path')
-def main(**kwargs):
-    learn(**kwargs)
-
-
-if __name__ == '__main__':
-    main()
+    return _sample_her_transitions

baselines/her/her_sampler.py

@@ -1,63 +0,0 @@
-import numpy as np
-
-
-def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
-    """Creates a sample function that can be used for HER experience replay.
-
-    Args:
-        replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
-            regular DDPG experience replay is used
-        replay_k (int): the ratio between HER replays and regular replays (e.g. k = 4 -> 4 times
-            as many HER replays as regular replays are used)
-        reward_fun (function): function to re-compute the reward with substituted goals
-    """
-    if replay_strategy == 'future':
-        future_p = 1 - (1. / (1 + replay_k))
-    else:  # 'replay_strategy' == 'none'
-        future_p = 0
-
-    def _sample_her_transitions(episode_batch, batch_size_in_transitions):
-        """episode_batch is {key: array(buffer_size x T x dim_key)}
-        """
-        T = episode_batch['u'].shape[1]
-        rollout_batch_size = episode_batch['u'].shape[0]
-        batch_size = batch_size_in_transitions
-
-        # Select which episodes and time steps to use.
-        episode_idxs = np.random.randint(0, rollout_batch_size, batch_size)
-        t_samples = np.random.randint(T, size=batch_size)
-        transitions = {key: episode_batch[key][episode_idxs, t_samples].copy()
-                       for key in episode_batch.keys()}
-
-        # Select future time indexes proportional with probability future_p. These
-        # will be used for HER replay by substituting in future goals.
-        her_indexes = np.where(np.random.uniform(size=batch_size) < future_p)
-        future_offset = np.random.uniform(size=batch_size) * (T - t_samples)
-        future_offset = future_offset.astype(int)
-        future_t = (t_samples + 1 + future_offset)[her_indexes]
-
-        # Replace goal with achieved goal but only for the previously-selected
-        # HER transitions (as defined by her_indexes). For the other transitions,
-        # keep the original goal.
-        future_ag = episode_batch['ag'][episode_idxs[her_indexes], future_t]
-        transitions['g'][her_indexes] = future_ag
-
-        # Reconstruct info dictionary for reward  computation.
-        info = {}
-        for key, value in transitions.items():
-            if key.startswith('info_'):
-                info[key.replace('info_', '')] = value
-
-        # Re-compute reward since we may have substituted the goal.
-        reward_params = {k: transitions[k] for k in ['ag_2', 'g']}
-        reward_params['info'] = info
-        transitions['r'] = reward_fun(**reward_params)
-
-        transitions = {k: transitions[k].reshape(batch_size, *transitions[k].shape[1:])
-                       for k in transitions.keys()}
-
-        assert(transitions['u'].shape[0] == batch_size_in_transitions)
-
-        return transitions
-
-    return _sample_her_transitions

baselines/her/rollout.py

@@ -2,6 +2,7 @@ from collections import deque
 
 import numpy as np
 import pickle
+from mujoco_py import MujocoException
 
 from baselines.her.util import convert_episode_to_batch_major, store_args
 
@@ -9,9 +10,9 @@ from baselines.her.util import convert_episode_to_batch_major, store_args
 class RolloutWorker:
 
     @store_args
-    def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=1,
+    def __init__(self, make_env, policy, dims, logger, T, rollout_batch_size=1,
                  exploit=False, use_target_net=False, compute_Q=False, noise_eps=0,
-                 random_eps=0, history_len=100, render=False, monitor=False, **kwargs):
+                 random_eps=0, history_len=100, render=False, **kwargs):
         """Rollout worker generates experience by interacting with one or many environments.
 
         Args:
@@ -30,7 +31,7 @@ class RolloutWorker:
             history_len (int): length of history for statistics smoothing
             render (boolean): whether or not to render the rollouts
         """
-
+        self.envs = [make_env() for _ in range(rollout_batch_size)]
         assert self.T > 0
 
         self.info_keys = [key.replace('info_', '') for key in dims.keys() if key.startswith('info_')]
@@ -39,14 +40,26 @@ class RolloutWorker:
         self.Q_history = deque(maxlen=history_len)
 
         self.n_episodes = 0
+        self.g = np.empty((self.rollout_batch_size, self.dims['g']), np.float32)  # goals
+        self.initial_o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32)  # observations
+        self.initial_ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32)  # achieved goals
         self.reset_all_rollouts()
         self.clear_history()
 
+    def reset_rollout(self, i):
+        """Resets the `i`-th rollout environment, re-samples a new goal, and updates the `initial_o`
+        and `g` arrays accordingly.
+        """
+        obs = self.envs[i].reset()
+        self.initial_o[i] = obs['observation']
+        self.initial_ag[i] = obs['achieved_goal']
+        self.g[i] = obs['desired_goal']
+
     def reset_all_rollouts(self):
-        self.obs_dict = self.venv.reset()
-        self.initial_o = self.obs_dict['observation']
-        self.initial_ag = self.obs_dict['achieved_goal']
-        self.g = self.obs_dict['desired_goal']
+        """Resets all `rollout_batch_size` rollout workers.
+        """
+        for i in range(self.rollout_batch_size):
+            self.reset_rollout(i)
 
     def generate_rollouts(self):
         """Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
@@ -62,8 +75,7 @@ class RolloutWorker:
 
         # generate episodes
         obs, achieved_goals, acts, goals, successes = [], [], [], [], []
-        dones = []
-        info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]
+        info_values = [np.empty((self.T, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]
         Qs = []
         for t in range(self.T):
             policy_output = self.policy.get_actions(
@@ -87,27 +99,27 @@ class RolloutWorker:
             ag_new = np.empty((self.rollout_batch_size, self.dims['g']))
             success = np.zeros(self.rollout_batch_size)
             # compute new states and observations
-            obs_dict_new, _, done, info = self.venv.step(u)
-            o_new = obs_dict_new['observation']
-            ag_new = obs_dict_new['achieved_goal']
-            success = np.array([i.get('is_success', 0.0) for i in info])
-
-            if any(done):
-                # here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done
-                # trick with using vecenvs is not to add the obs from the environments that are "done", because those are already observations
-                # after a reset
-                break
-
-            for i, info_dict in enumerate(info):
-                for idx, key in enumerate(self.info_keys):
-                    info_values[idx][t, i] = info[i][key]
+            for i in range(self.rollout_batch_size):
+                try:
+                    # We fully ignore the reward here because it will have to be re-computed
+                    # for HER.
+                    curr_o_new, _, _, info = self.envs[i].step(u[i])
+                    if 'is_success' in info:
+                        success[i] = info['is_success']
+                    o_new[i] = curr_o_new['observation']
+                    ag_new[i] = curr_o_new['achieved_goal']
+                    for idx, key in enumerate(self.info_keys):
+                        info_values[idx][t, i] = info[key]
+                    if self.render:
+                        self.envs[i].render()
+                except MujocoException as e:
+                    return self.generate_rollouts()
 
             if np.isnan(o_new).any():
                 self.logger.warn('NaN caught during rollout generation. Trying again...')
                 self.reset_all_rollouts()
                 return self.generate_rollouts()
 
-            dones.append(done)
             obs.append(o.copy())
             achieved_goals.append(ag.copy())
             successes.append(success.copy())
@@ -117,6 +129,7 @@ class RolloutWorker:
             ag[...] = ag_new
         obs.append(o.copy())
         achieved_goals.append(ag.copy())
+        self.initial_o[:] = o
 
         episode = dict(o=obs,
                        u=acts,
@@ -163,8 +176,13 @@ class RolloutWorker:
             logs += [('mean_Q', np.mean(self.Q_history))]
         logs += [('episode', self.n_episodes)]
 
-        if prefix != '' and not prefix.endswith('/'):
+        if prefix is not '' and not prefix.endswith('/'):
             return [(prefix + '/' + key, val) for key, val in logs]
         else:
             return logs
 
+    def seed(self, seed):
+        """Seeds each environment with a distinct seed derived from the passed in global seed.
+        """
+        for idx, env in enumerate(self.envs):
+            env.seed(seed + 1000 * idx)

baselines/logger.py

@@ -54,7 +54,7 @@ class HumanOutputFormat(KVWriter, SeqWriter):
         # Write out the data
         dashes = '-' * (keywidth + valwidth + 7)
         lines = [dashes]
-        for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
+        for (key, val) in sorted(key2str.items()):
             lines.append('| %s%s | %s%s |' % (
                 key,
                 ' ' * (keywidth - len(key)),

baselines/ppo1/pposgd_simple.py

@@ -97,7 +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
+    clip_param = clip_param * lrmult # Annealed cliping parameter epislon
 
     ob = U.get_placeholder_cached(name="ob")
     ac = pi.pdtype.sample_placeholder([None])

baselines/ppo2/microbatched_model.py

@@ -1,76 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from baselines.ppo2.model import Model
-
-class MicrobatchedModel(Model):
-    """
-    Model that does training one microbatch at a time - when gradient computation
-    on the entire minibatch causes some overflow
-    """
-    def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
-                nsteps, ent_coef, vf_coef, max_grad_norm, microbatch_size):
-
-        self.nmicrobatches = nbatch_train // microbatch_size
-        self.microbatch_size = microbatch_size
-        assert nbatch_train % microbatch_size == 0, 'microbatch_size ({}) should divide nbatch_train ({}) evenly'.format(microbatch_size, nbatch_train)
-
-        super().__init__(
-                policy=policy,
-                ob_space=ob_space,
-                ac_space=ac_space,
-                nbatch_act=nbatch_act,
-                nbatch_train=microbatch_size,
-                nsteps=nsteps,
-                ent_coef=ent_coef,
-                vf_coef=vf_coef,
-                max_grad_norm=max_grad_norm)
-
-        self.grads_ph = [tf.placeholder(dtype=g.dtype, shape=g.shape) for g in self.grads]
-        grads_ph_and_vars = list(zip(self.grads_ph, self.var))
-        self._apply_gradients_op = self.trainer.apply_gradients(grads_ph_and_vars)
-
-
-    def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):
-        assert states is None, "microbatches with recurrent models are not supported yet"
-
-        # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
-        # Returns = R + yV(s')
-        advs = returns - values
-
-        # Normalize the advantages
-        advs = (advs - advs.mean()) / (advs.std() + 1e-8)
-
-        # Initialize empty list for per-microbatch stats like pg_loss, vf_loss, entropy, approxkl (whatever is in self.stats_list)
-        stats_vs = []
-
-        for microbatch_idx in range(self.nmicrobatches):
-            _sli = range(microbatch_idx * self.microbatch_size, (microbatch_idx+1) * self.microbatch_size)
-            td_map = {
-                self.train_model.X: obs[_sli],
-                self.A:actions[_sli],
-                self.ADV:advs[_sli],
-                self.R:returns[_sli],
-                self.CLIPRANGE:cliprange,
-                self.OLDNEGLOGPAC:neglogpacs[_sli],
-                self.OLDVPRED:values[_sli]
-            }
-
-            # Compute gradient on a microbatch (note that variables do not change here) ...
-            grad_v, stats_v  = self.sess.run([self.grads, self.stats_list], td_map)
-            if microbatch_idx == 0:
-                sum_grad_v = grad_v
-            else:
-                # .. and add to the total of the gradients
-                for i, g in enumerate(grad_v):
-                    sum_grad_v[i] += g
-            stats_vs.append(stats_v)
-
-        feed_dict = {ph: sum_g / self.nmicrobatches for ph, sum_g in zip(self.grads_ph, sum_grad_v)}
-        feed_dict[self.LR] = lr
-        # Update variables using average of the gradients
-        self.sess.run(self._apply_gradients_op, feed_dict)
-        # Return average of the stats
-        return np.mean(np.array(stats_vs), axis=0).tolist()
-
-
-

baselines/ppo2/model.py

@@ -1,156 +0,0 @@
-import tensorflow as tf
-import functools
-
-from baselines.common.tf_util import get_session, save_variables, load_variables
-from baselines.common.tf_util import initialize
-
-try:
-    from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
-    from mpi4py import MPI
-    from baselines.common.mpi_util import sync_from_root
-except ImportError:
-    MPI = None
-
-class Model(object):
-    """
-    We use this object to :
-    __init__:
-    - Creates the step_model
-    - Creates the train_model
-
-    train():
-    - Make the training part (feedforward and retropropagation of gradients)
-
-    save/load():
-    - Save load the model
-    """
-    def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
-                nsteps, ent_coef, vf_coef, max_grad_norm, microbatch_size=None):
-        self.sess = sess = get_session()
-
-        with tf.variable_scope('ppo2_model', reuse=tf.AUTO_REUSE):
-            # CREATE OUR TWO MODELS
-            # act_model that is used for sampling
-            act_model = policy(nbatch_act, 1, sess)
-
-            # Train model for training
-            if microbatch_size is None:
-                train_model = policy(nbatch_train, nsteps, sess)
-            else:
-                train_model = policy(microbatch_size, nsteps, sess)
-
-        # CREATE THE PLACEHOLDERS
-        self.A = A = train_model.pdtype.sample_placeholder([None])
-        self.ADV = ADV = tf.placeholder(tf.float32, [None])
-        self.R = R = tf.placeholder(tf.float32, [None])
-        # Keep track of old actor
-        self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])
-        # Keep track of old critic
-        self.OLDVPRED = OLDVPRED = tf.placeholder(tf.float32, [None])
-        self.LR = LR = tf.placeholder(tf.float32, [])
-        # Cliprange
-        self.CLIPRANGE = CLIPRANGE = tf.placeholder(tf.float32, [])
-
-        neglogpac = train_model.pd.neglogp(A)
-
-        # Calculate the entropy
-        # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
-        entropy = tf.reduce_mean(train_model.pd.entropy())
-
-        # CALCULATE THE LOSS
-        # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
-
-        # Clip the value to reduce variability during Critic training
-        # Get the predicted value
-        vpred = train_model.vf
-        vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE)
-        # Unclipped value
-        vf_losses1 = tf.square(vpred - R)
-        # Clipped value
-        vf_losses2 = tf.square(vpredclipped - R)
-
-        vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
-
-        # Calculate ratio (pi current policy / pi old policy)
-        ratio = tf.exp(OLDNEGLOGPAC - neglogpac)
-
-        # Defining Loss = - J is equivalent to max J
-        pg_losses = -ADV * ratio
-
-        pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)
-
-        # Final PG loss
-        pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))
-        approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))
-        clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
-
-        # Total loss
-        loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
-
-        # UPDATE THE PARAMETERS USING LOSS
-        # 1. Get the model parameters
-        params = tf.trainable_variables('ppo2_model')
-        # 2. Build our trainer
-        if MPI is not None:
-            self.trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
-        else:
-            self.trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
-        # 3. Calculate the gradients
-        grads_and_var = self.trainer.compute_gradients(loss, params)
-        grads, var = zip(*grads_and_var)
-
-        if max_grad_norm is not None:
-            # Clip the gradients (normalize)
-            grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
-        grads_and_var = list(zip(grads, var))
-        # zip aggregate each gradient with parameters associated
-        # For instance zip(ABCD, xyza) => Ax, By, Cz, Da
-
-        self.grads = grads
-        self.var = var
-        self._train_op = self.trainer.apply_gradients(grads_and_var)
-        self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']
-        self.stats_list = [pg_loss, vf_loss, entropy, approxkl, clipfrac]
-
-
-        self.train_model = train_model
-        self.act_model = act_model
-        self.step = act_model.step
-        self.value = act_model.value
-        self.initial_state = act_model.initial_state
-
-        self.save = functools.partial(save_variables, sess=sess)
-        self.load = functools.partial(load_variables, sess=sess)
-
-        initialize()
-        global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
-        if MPI is not None:
-            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)
-        # Returns = R + yV(s')
-        advs = returns - values
-
-        # Normalize the advantages
-        advs = (advs - advs.mean()) / (advs.std() + 1e-8)
-
-        td_map = {
-            self.train_model.X : obs,
-            self.A : actions,
-            self.ADV : advs,
-            self.R : returns,
-            self.LR : lr,
-            self.CLIPRANGE : cliprange,
-            self.OLDNEGLOGPAC : neglogpacs,
-            self.OLDVPRED : values
-        }
-        if states is not None:
-            td_map[self.train_model.S] = states
-            td_map[self.train_model.M] = masks
-
-        return self.sess.run(
-            self.stats_list + [self._train_op],
-            td_map
-        )[:-1]
-

baselines/ppo2/ppo2.py

@@ -1,17 +1,226 @@
 import os
 import time
+import functools
 import numpy as np
 import os.path as osp
+import tensorflow as tf
 from baselines import logger
 from collections import deque
 from baselines.common import explained_variance, set_global_seeds
 from baselines.common.policies import build_policy
+from baselines.common.runners import AbstractEnvRunner
+from baselines.common.tf_util import get_session, save_variables, load_variables
+
 try:
+    from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
     from mpi4py import MPI
+    from baselines.common.mpi_util import sync_from_root
 except ImportError:
     MPI = None
-from baselines.ppo2.runner import Runner
 
+from baselines.common.tf_util import initialize
+
+class Model(object):
+    """
+    We use this object to :
+    __init__:
+    - Creates the step_model
+    - Creates the train_model
+
+    train():
+    - Make the training part (feedforward and retropropagation of gradients)
+
+    save/load():
+    - Save load the model
+    """
+    def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,
+                nsteps, ent_coef, vf_coef, max_grad_norm):
+        sess = get_session()
+
+        with tf.variable_scope('ppo2_model', reuse=tf.AUTO_REUSE):
+            # CREATE OUR TWO MODELS
+            # act_model that is used for sampling
+            act_model = policy(nbatch_act, 1, sess)
+
+            # Train model for training
+            train_model = policy(nbatch_train, nsteps, sess)
+
+        # CREATE THE PLACEHOLDERS
+        A = train_model.pdtype.sample_placeholder([None])
+        ADV = tf.placeholder(tf.float32, [None])
+        R = tf.placeholder(tf.float32, [None])
+        # Keep track of old actor
+        OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])
+        # Keep track of old critic
+        OLDVPRED = tf.placeholder(tf.float32, [None])
+        LR = tf.placeholder(tf.float32, [])
+        # Cliprange
+        CLIPRANGE = tf.placeholder(tf.float32, [])
+
+        neglogpac = train_model.pd.neglogp(A)
+
+        # Calculate the entropy
+        # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
+        entropy = tf.reduce_mean(train_model.pd.entropy())
+
+        # CALCULATE THE LOSS
+        # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
+
+        # Clip the value to reduce variability during Critic training
+        # Get the predicted value
+        vpred = train_model.vf
+        vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE)
+        # Unclipped value
+        vf_losses1 = tf.square(vpred - R)
+        # Clipped value
+        vf_losses2 = tf.square(vpredclipped - R)
+
+        vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
+
+        # Calculate ratio (pi current policy / pi old policy)
+        ratio = tf.exp(OLDNEGLOGPAC - neglogpac)
+
+        # Defining Loss = - J is equivalent to max J
+        pg_losses = -ADV * ratio
+
+        pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)
+
+        # Final PG loss
+        pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))
+        approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))
+        clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
+
+        # Total loss
+        loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
+
+        # UPDATE THE PARAMETERS USING LOSS
+        # 1. Get the model parameters
+        params = tf.trainable_variables('ppo2_model')
+        # 2. Build our trainer
+        if MPI is not None:
+            trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+        else:
+            trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
+        # 3. Calculate the gradients
+        grads_and_var = trainer.compute_gradients(loss, params)
+        grads, var = zip(*grads_and_var)
+
+        if max_grad_norm is not None:
+            # Clip the gradients (normalize)
+            grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
+        grads_and_var = list(zip(grads, var))
+        # zip aggregate each gradient with parameters associated
+        # For instance zip(ABCD, xyza) => Ax, By, Cz, Da
+
+        _train = trainer.apply_gradients(grads_and_var)
+
+        def train(lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):
+            # Here we calculate advantage A(s,a) = R + yV(s') - V(s)
+            # Returns = R + yV(s')
+            advs = returns - values
+
+            # Normalize the advantages
+            advs = (advs - advs.mean()) / (advs.std() + 1e-8)
+            td_map = {train_model.X:obs, A:actions, ADV:advs, R:returns, LR:lr,
+                    CLIPRANGE:cliprange, OLDNEGLOGPAC:neglogpacs, OLDVPRED:values}
+            if states is not None:
+                td_map[train_model.S] = states
+                td_map[train_model.M] = masks
+            return sess.run(
+                [pg_loss, vf_loss, entropy, approxkl, clipfrac, _train],
+                td_map
+            )[:-1]
+        self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']
+
+
+        self.train = train
+        self.train_model = train_model
+        self.act_model = act_model
+        self.step = act_model.step
+        self.value = act_model.value
+        self.initial_state = act_model.initial_state
+
+        self.save = functools.partial(save_variables, sess=sess)
+        self.load = functools.partial(load_variables, sess=sess)
+
+        if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
+            initialize()
+        global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
+
+        if MPI is not None:
+            sync_from_root(sess, global_variables) #pylint: disable=E1101
+
+class Runner(AbstractEnvRunner):
+    """
+    We use this object to make a mini batch of experiences
+    __init__:
+    - Initialize the runner
+
+    run():
+    - Make a mini batch
+    """
+    def __init__(self, *, env, model, nsteps, gamma, lam):
+        super().__init__(env=env, model=model, nsteps=nsteps)
+        # Lambda used in GAE (General Advantage Estimation)
+        self.lam = lam
+        # Discount rate
+        self.gamma = gamma
+
+    def run(self):
+        # Here, we init the lists that will contain the mb of experiences
+        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]
+        mb_states = self.states
+        epinfos = []
+        # For n in range number of steps
+        for _ in range(self.nsteps):
+            # Given observations, get action value and neglopacs
+            # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
+            actions, values, self.states, neglogpacs = self.model.step(self.obs, S=self.states, M=self.dones)
+            mb_obs.append(self.obs.copy())
+            mb_actions.append(actions)
+            mb_values.append(values)
+            mb_neglogpacs.append(neglogpacs)
+            mb_dones.append(self.dones)
+
+            # Take actions in env and look the results
+            # Infos contains a ton of useful informations
+            self.obs[:], rewards, self.dones, infos = self.env.step(actions)
+            for info in infos:
+                maybeepinfo = info.get('episode')
+                if maybeepinfo: epinfos.append(maybeepinfo)
+            mb_rewards.append(rewards)
+        #batch of steps to batch of rollouts
+        mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)
+        mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
+        mb_actions = np.asarray(mb_actions)
+        mb_values = np.asarray(mb_values, dtype=np.float32)
+        mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)
+        mb_dones = np.asarray(mb_dones, dtype=np.bool)
+        last_values = self.model.value(self.obs, S=self.states, M=self.dones)
+
+        # discount/bootstrap off value fn
+        mb_returns = np.zeros_like(mb_rewards)
+        mb_advs = np.zeros_like(mb_rewards)
+        lastgaelam = 0
+        for t in reversed(range(self.nsteps)):
+            if t == self.nsteps - 1:
+                nextnonterminal = 1.0 - self.dones
+                nextvalues = last_values
+            else:
+                nextnonterminal = 1.0 - mb_dones[t+1]
+                nextvalues = mb_values[t+1]
+            delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]
+            mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam
+        mb_returns = mb_advs + mb_values
+        return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),
+            mb_states, epinfos)
+# obs, returns, masks, actions, values, neglogpacs, states = runner.run()
+def sf01(arr):
+    """
+    swap and then flatten axes 0 and 1
+    """
+    s = arr.shape
+    return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
 
 def constfn(val):
     def f(_):
@@ -21,7 +230,7 @@ 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, **network_kwargs):
+            save_interval=0, load_path=None, **network_kwargs):
     '''
     Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
 
@@ -99,14 +308,10 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
     nbatch_train = nbatch // nminibatches
 
     # Instantiate the model object (that creates act_model and train_model)
-    if model_fn is None:
-        from baselines.ppo2.model import Model
-        model_fn = Model
-
-    model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
+    make_model = lambda : Model(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)
-
+    model = make_model()
     if load_path is not None:
         model.load(load_path)
     # Instantiate the runner object
@@ -114,6 +319,8 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
     if eval_env is not None:
         eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
 
+
+
     epinfobuf = deque(maxlen=100)
     if eval_env is not None:
         eval_epinfobuf = deque(maxlen=100)

baselines/ppo2/runner.py

@@ -1,76 +0,0 @@
-import numpy as np
-from baselines.common.runners import AbstractEnvRunner
-
-class Runner(AbstractEnvRunner):
-    """
-    We use this object to make a mini batch of experiences
-    __init__:
-    - Initialize the runner
-
-    run():
-    - Make a mini batch
-    """
-    def __init__(self, *, env, model, nsteps, gamma, lam):
-        super().__init__(env=env, model=model, nsteps=nsteps)
-        # Lambda used in GAE (General Advantage Estimation)
-        self.lam = lam
-        # Discount rate
-        self.gamma = gamma
-
-    def run(self):
-        # Here, we init the lists that will contain the mb of experiences
-        mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]
-        mb_states = self.states
-        epinfos = []
-        # For n in range number of steps
-        for _ in range(self.nsteps):
-            # Given observations, get action value and neglopacs
-            # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init
-            actions, values, self.states, neglogpacs = self.model.step(self.obs, S=self.states, M=self.dones)
-            mb_obs.append(self.obs.copy())
-            mb_actions.append(actions)
-            mb_values.append(values)
-            mb_neglogpacs.append(neglogpacs)
-            mb_dones.append(self.dones)
-
-            # Take actions in env and look the results
-            # Infos contains a ton of useful informations
-            self.obs[:], rewards, self.dones, infos = self.env.step(actions)
-            for info in infos:
-                maybeepinfo = info.get('episode')
-                if maybeepinfo: epinfos.append(maybeepinfo)
-            mb_rewards.append(rewards)
-        #batch of steps to batch of rollouts
-        mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)
-        mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
-        mb_actions = np.asarray(mb_actions)
-        mb_values = np.asarray(mb_values, dtype=np.float32)
-        mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)
-        mb_dones = np.asarray(mb_dones, dtype=np.bool)
-        last_values = self.model.value(self.obs, S=self.states, M=self.dones)
-
-        # discount/bootstrap off value fn
-        mb_returns = np.zeros_like(mb_rewards)
-        mb_advs = np.zeros_like(mb_rewards)
-        lastgaelam = 0
-        for t in reversed(range(self.nsteps)):
-            if t == self.nsteps - 1:
-                nextnonterminal = 1.0 - self.dones
-                nextvalues = last_values
-            else:
-                nextnonterminal = 1.0 - mb_dones[t+1]
-                nextvalues = mb_values[t+1]
-            delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]
-            mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam
-        mb_returns = mb_advs + mb_values
-        return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),
-            mb_states, epinfos)
-# obs, returns, masks, actions, values, neglogpacs, states = runner.run()
-def sf01(arr):
-    """
-    swap and then flatten axes 0 and 1
-    """
-    s = arr.shape
-    return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
-
-

baselines/ppo2/test_microbatches.py

@@ -1,34 +0,0 @@
-import gym
-import tensorflow as tf
-import numpy as np
-from functools import partial
-
-from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-from baselines.common.tf_util import make_session
-from baselines.ppo2.ppo2 import learn
-
-from baselines.ppo2.microbatched_model import MicrobatchedModel
-
-def test_microbatches():
-    def env_fn():
-        env = gym.make('CartPole-v0')
-        env.seed(0)
-        return env
-
-    learn_fn = partial(learn, network='mlp', nsteps=32, total_timesteps=32, seed=0)
-
-    env_ref = DummyVecEnv([env_fn])
-    sess_ref = make_session(make_default=True, graph=tf.Graph())
-    learn_fn(env=env_ref)
-    vars_ref = {v.name: sess_ref.run(v) for v in tf.trainable_variables()}
-
-    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))
-    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=1e-3)
-
-if __name__ == '__main__':
-    test_microbatches()

baselines/results_plotter.py

@@ -5,7 +5,7 @@ matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
 import matplotlib.pyplot as plt
 plt.rcParams['svg.fonttype'] = 'none'
 
-from baselines.common import plot_util
+from baselines.bench.monitor import load_results
 
 X_TIMESTEPS = 'timesteps'
 X_EPISODES = 'episodes'
@@ -16,7 +16,7 @@ POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
 EPISODES_WINDOW = 100
 COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
         'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
-        'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue']
+        'darkgreen', 'tan', 'salmon', 'gold', 'lightpurple', 'darkred', 'darkblue']
 
 def rolling_window(a, window):
     shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
@@ -50,7 +50,7 @@ def plot_curves(xy_list, xaxis, yaxis, title):
     maxx = max(xy[0][-1] for xy in xy_list)
     minx = 0
     for (i, (x, y)) in enumerate(xy_list):
-        color = COLORS[i % len(COLORS)]
+        color = COLORS[i]
         plt.scatter(x, y, s=2)
         x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes
         plt.plot(x, y_mean, color=color)
@@ -62,18 +62,19 @@ def plot_curves(xy_list, xaxis, yaxis, title):
     fig.canvas.mpl_connect('resize_event', lambda event: plt.tight_layout())
     plt.grid(True)
 
-
-def split_by_task(taskpath):
-    return taskpath['dirname'].split('/')[-1].split('-')[0]
-
-def plot_results(dirs, num_timesteps=10e6, xaxis=X_TIMESTEPS, yaxis=Y_REWARD, title='', split_fn=split_by_task):
-    results = plot_util.load_results(dirs)
-    plot_util.plot_results(results, xy_fn=lambda r: ts2xy(r['monitor'], xaxis, yaxis), split_fn=split_fn, average_group=True, resample=int(1e6))
+def plot_results(dirs, num_timesteps, xaxis, yaxis, task_name):
+    tslist = []
+    for dir in dirs:
+        ts = load_results(dir)
+        ts = ts[ts.l.cumsum() <= num_timesteps]
+        tslist.append(ts)
+    xy_list = [ts2xy(ts, xaxis, yaxis) for ts in tslist]
+    plot_curves(xy_list, xaxis, yaxis, task_name)
 
 # Example usage in jupyter-notebook
-# from baselines.results_plotter import plot_results
+# from baselines import log_viewer
 # %matplotlib inline
-# plot_results("./log")
+# log_viewer.plot_results(["./log"], 10e6, log_viewer.X_TIMESTEPS, "Breakout")
 # Here ./log is a directory containing the monitor.csv files
 
 def main():

baselines/run.py

@@ -110,8 +110,7 @@ def build_env(args):
        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)
+       env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale)
 
        if env_type == 'mujoco':
            env = VecNormalize(env)
@@ -120,11 +119,6 @@ def build_env(args):
 
 
 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]
-        _game_envs[env_type].add(env.id)  # This is a set so add is idempotent
-
     if env_id in _game_envs.keys():
         env_type = env_id
         env_id = [g for g in _game_envs[env_type]][0]
@@ -187,16 +181,13 @@ def parse_cmdline_kwargs(args):
 
 
 
-def main(args):
+def main():
     # configure logger, disable logging in child MPI processes (with rank > 0)
 
     arg_parser = common_arg_parser()
-    args, unknown_args = arg_parser.parse_known_args(args)
+    args, unknown_args = arg_parser.parse_known_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()
@@ -215,16 +206,11 @@ def main(args):
         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,))
-
+        def initialize_placeholders(nlstm=128,**kwargs):
+            return np.zeros((args.num_env or 1, 2*nlstm)), np.zeros((1))
+        state, dones = initialize_placeholders(**extra_args)
         while True:
-            if state is not None:
-                actions, _, state, _ = model.step(obs,S=state, M=dones)
-            else:
-                actions, _, _, _ = model.step(obs)
-
+            actions, _, state, _ = model.step(obs,S=state, M=dones)
             obs, _, done, _ = env.step(actions)
             env.render()
             done = done.any() if isinstance(done, np.ndarray) else done
@@ -234,7 +220,5 @@ def main(args):
 
         env.close()
 
-    return model
-
 if __name__ == '__main__':
-    main(sys.argv)
+    main()

docs/viz/viz.ipynb

@@ -7,7 +7,7 @@
     "id": "Ynb-laSwmpac"
    },
    "source": [
-    "# Loading and visualizing results ([open in colab](https://colab.research.google.com/github/openai/baselines/blob/master/docs/viz/viz.ipynb))\n",
+    "# Loading and visualizing results ([open in colab](https://colab.research.google.com/github/openai/baselines/blob/master/docs/viz.ipynb))\n",
     "In order to compare performance of algorithms, we often would like to visualize learning curves (reward as a function of time steps), or some other auxiliary information about learning aggregated into a plot. Baselines repo provides tools for doing so in several different ways, depending on the goal."
    ]
   },

setup.cfg

@@ -3,5 +3,6 @@ select = F,E999,W291,W293
 exclude = 
     .git,
     __pycache__,
+    baselines/her,
     baselines/ppo1,
     baselines/bench,

setup.py

@@ -53,11 +53,11 @@ setup(name='baselines',
 # ensure there is some tensorflow build with version above 1.4
 import pkg_resources
 tf_pkg = None
-for tf_pkg_name in ['tensorflow', 'tensorflow-gpu', 'tf-nightly', 'tf-nightly-gpu']:
+for tf_pkg_name in ['tensorflow', 'tensorflow-gpu']:
     try:
         tf_pkg = pkg_resources.get_distribution(tf_pkg_name)
     except pkg_resources.DistributionNotFound:
         pass
 assert tf_pkg is not None, 'TensorFlow needed, of version above 1.4'
-from distutils.version import LooseVersion
-assert LooseVersion(re.sub(r'-?rc\d+$', '', tf_pkg.version)) >= LooseVersion('1.4.0')
+from distutils.version import StrictVersion
+assert StrictVersion(re.sub(r'-?rc\d+$', '', tf_pkg.version)) >= StrictVersion('1.4.0')