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/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/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):
@@ -82,7 +82,7 @@ class MultiCategoricalPdType(PdType):
         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):
@@ -99,7 +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
@@ -123,7 +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
@@ -345,9 +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/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_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/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)

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/her/ddpg.py

@@ -367,6 +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)

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

@@ -181,11 +181,11 @@ 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 MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
@@ -220,7 +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."
    ]
   },