[rllib] Support batch norm layers

doc/source/rllib-models.rst

@@ -30,7 +30,7 @@ The following is a list of the built-in model hyperparameters:
 Custom Models
 -------------
 
-Custom models should subclass the common RLlib `model class <https://github.com/ray-project/ray/blob/master/python/ray/rllib/models/model.py>`__ and override the ``_build_layers_v2`` method. This method takes in a dict of tensor inputs (the observation ``obs``, ``prev_action``, and ``prev_reward``), and returns a feature layer and float vector of the specified output size. You can also override the ``value_function`` method to implement a custom value branch. A self-supervised loss can be defined via the ``loss`` method. The model can then be registered and used in place of a built-in model:
+Custom models should subclass the common RLlib `model class <https://github.com/ray-project/ray/blob/master/python/ray/rllib/models/model.py>`__ and override the ``_build_layers_v2`` method. This method takes in a dict of tensor inputs (the observation ``obs``, ``prev_action``, and ``prev_reward``, ``is_training``), and returns a feature layer and float vector of the specified output size. You can also override the ``value_function`` method to implement a custom value branch. A self-supervised loss can be defined via the ``loss`` method. The model can then be registered and used in place of a built-in model:
 
 .. code-block:: python
 
@@ -44,7 +44,7 @@ Custom models should subclass the common RLlib `model class <https://github.com/
 
             Arguments:
                 input_dict (dict): Dictionary of input tensors, including "obs",
-                    "prev_action", "prev_reward".
+                    "prev_action", "prev_reward", "is_training".
                 num_outputs (int): Output tensor must be of size
                     [BATCH_SIZE, num_outputs].
                 options (dict): Model options.
@@ -60,6 +60,7 @@ Custom models should subclass the common RLlib `model class <https://github.com/
                 >>> print(input_dict)
                 {'prev_actions': <tf.Tensor shape=(?,) dtype=int64>,
                  'prev_rewards': <tf.Tensor shape=(?,) dtype=float32>,
+                 'is_training': <tf.Tensor shape=(), dtype=bool>,
                  'obs': OrderedDict([
                     ('sensors', OrderedDict([
                         ('front_cam', [
@@ -115,7 +116,6 @@ Custom Recurrent Models
 
 Instead of using the ``use_lstm: True`` option, it can be preferable use a custom recurrent model. This provides more control over postprocessing of the LSTM output and can also allow the use of multiple LSTM cells to process different portions of the input. The only difference from a normal custom model is that you have to define ``self.state_init``, ``self.state_in``, and ``self.state_out``. You can refer to the existing `lstm.py <https://github.com/ray-project/ray/blob/master/python/ray/rllib/models/lstm.py>`__ model as an example to implement your own model:
 
-
 .. code-block:: python
 
     class MyCustomLSTM(Model):
@@ -147,6 +147,11 @@ Instead of using the ``use_lstm: True`` option, it can be preferable use a custo
                             normc_initializer(0.01))
             return logits, last_layer
 
+Batch Normalization
+~~~~~~~~~~~~~~~~~~~
+
+You can use ``tf.layers.batch_normalization(x, training=input_dict["is_training"])`` to add batch norm layers to your custom model: `code example <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/batch_norm_model.py>`__. RLlib will automatically run the update ops for the batch norm layers during optimization (see `tf_policy_graph.py <https://github.com/ray-project/ray/blob/master/python/ray/rllib/evaluation/tf_policy_graph.py>`__ and `multi_gpu_impl.py <https://github.com/ray-project/ray/blob/master/python/ray/rllib/optimizers/multi_gpu_impl.py>`__ for the exact handling of these updates).
+
 Custom Preprocessors
 --------------------
 
@@ -283,7 +288,8 @@ With a custom policy graph, you can also perform model-based rollouts and option
              def compute_actions(self,
                                  obs_batch,
                                  state_batches,
-                                 is_training=False,
+                                 prev_action_batch=None,
+                                 prev_reward_batch=None,
                                  episodes=None):
                 # compute a batch of actions based on the current obs_batch
                 # and state of each episode (i.e., for multiagent). You can do

python/ray/rllib/agents/a3c/a3c_tf_policy_graph.py

@@ -53,7 +53,8 @@ def __init__(self, observation_space, action_space, config):
         self.model = ModelCatalog.get_model({
             "obs": self.observations,
             "prev_actions": prev_actions,
-            "prev_rewards": prev_rewards
+            "prev_rewards": prev_rewards,
+            "is_training": self._get_is_training_placeholder(),
         }, observation_space, logit_dim, self.config["model"])
         action_dist = dist_class(self.model.outputs)
         self.vf = self.model.value_function()

python/ray/rllib/agents/agent.py

@@ -385,13 +385,11 @@ def compute_action(self, observation, state=None, policy_id="default"):
             observation, update=False)
         if state:
             return self.local_evaluator.for_policy(
-                lambda p: p.compute_single_action(
-                    filtered_obs, state, is_training=False),
+                lambda p: p.compute_single_action(filtered_obs, state),
                 policy_id=policy_id)
         return self.local_evaluator.for_policy(
-                lambda p: p.compute_single_action(
-                    filtered_obs, state, is_training=False)[0],
-                policy_id=policy_id)
+            lambda p: p.compute_single_action(filtered_obs, state)[0],
+            policy_id=policy_id)
 
     def get_weights(self, policies=None):
         """Return a dictionary of policy ids to weights.

python/ray/rllib/agents/ddpg/ddpg_policy_graph.py

@@ -199,7 +199,9 @@ def __init__(self, observation_space, action_space, config):
         self.stochastic = tf.placeholder(tf.bool, (), name="stochastic")
         self.eps = tf.placeholder(tf.float32, (), name="eps")
         self.cur_observations = tf.placeholder(
-            tf.float32, shape=(None, ) + observation_space.shape)
+            tf.float32,
+            shape=(None, ) + observation_space.shape,
+            name="cur_obs")
 
         # Actor: P (policy) network
         with tf.variable_scope(P_SCOPE) as scope:
@@ -236,7 +238,11 @@ def __init__(self, observation_space, action_space, config):
 
         # p network evaluation
         with tf.variable_scope(P_SCOPE, reuse=True) as scope:
+            prev_update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
             self.p_t = self._build_p_network(self.obs_t, observation_space)
+            p_batchnorm_update_ops = list(
+                set(tf.get_collection(tf.GraphKeys.UPDATE_OPS)) -
+                prev_update_ops)
 
         # target p network evaluation
         with tf.variable_scope(P_TARGET_SCOPE) as scope:
@@ -257,6 +263,7 @@ def __init__(self, observation_space, action_space, config):
                 is_target=True)
 
         # q network evaluation
+        prev_update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
         with tf.variable_scope(Q_SCOPE) as scope:
             q_t, model = self._build_q_network(self.obs_t, observation_space,
                                                self.act_t)
@@ -269,6 +276,8 @@ def __init__(self, observation_space, action_space, config):
                 twin_q_t, twin_model = self._build_q_network(
                     self.obs_t, observation_space, self.act_t)
                 self.twin_q_func_vars = _scope_vars(scope.name)
+        q_batchnorm_update_ops = list(
+            set(tf.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
 
         # target q network evalution
         with tf.variable_scope(Q_TARGET_SCOPE) as scope:
@@ -345,7 +354,8 @@ def __init__(self, observation_space, action_space, config):
             obs_input=self.cur_observations,
             action_sampler=self.output_actions,
             loss=model.loss() + self.loss.total_loss,
-            loss_inputs=self.loss_inputs)
+            loss_inputs=self.loss_inputs,
+            update_ops=q_batchnorm_update_ops + p_batchnorm_update_ops)
         self.sess.run(tf.global_variables_initializer())
 
         # Note that this encompasses both the policy and Q-value networks and
@@ -359,7 +369,8 @@ def __init__(self, observation_space, action_space, config):
     def _build_q_network(self, obs, obs_space, actions):
         q_net = QNetwork(
             ModelCatalog.get_model({
-                "obs": obs
+                "obs": obs,
+                "is_training": self._get_is_training_placeholder(),
             }, obs_space, 1, self.config["model"]), actions,
             self.config["critic_hiddens"],
             self.config["critic_hidden_activation"])
@@ -368,7 +379,8 @@ def _build_q_network(self, obs, obs_space, actions):
     def _build_p_network(self, obs, obs_space):
         return PNetwork(
             ModelCatalog.get_model({
-                "obs": obs
+                "obs": obs,
+                "is_training": self._get_is_training_placeholder(),
             }, obs_space, 1, self.config["model"]), self.dim_actions,
             self.config["actor_hiddens"],
             self.config["actor_hidden_activation"]).action_scores

python/ray/rllib/agents/dqn/dqn_policy_graph.py

@@ -306,8 +306,12 @@ def __init__(self, observation_space, action_space, config):
 
         # q network evaluation
         with tf.variable_scope(Q_SCOPE, reuse=True):
+            prev_update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
             q_t, q_logits_t, q_dist_t, model = self._build_q_network(
                 self.obs_t, observation_space)
+            q_batchnorm_update_ops = list(
+                set(tf.get_collection(tf.GraphKeys.UPDATE_OPS)) -
+                prev_update_ops)
 
         # target q network evalution
         with tf.variable_scope(Q_TARGET_SCOPE) as scope:
@@ -372,13 +376,15 @@ def __init__(self, observation_space, action_space, config):
             obs_input=self.cur_observations,
             action_sampler=self.output_actions,
             loss=model.loss() + self.loss.loss,
-            loss_inputs=self.loss_inputs)
+            loss_inputs=self.loss_inputs,
+            update_ops=q_batchnorm_update_ops)
         self.sess.run(tf.global_variables_initializer())
 
     def _build_q_network(self, obs, space):
         qnet = QNetwork(
             ModelCatalog.get_model({
-                "obs": obs
+                "obs": obs,
+                "is_training": self._get_is_training_placeholder(),
             }, space, self.num_actions, self.config["model"]),
             self.num_actions, self.config["dueling"], self.config["hiddens"],
             self.config["noisy"], self.config["num_atoms"],

python/ray/rllib/agents/impala/vtrace_policy_graph.py

@@ -133,6 +133,7 @@ def __init__(self,
                 "obs": observations,
                 "prev_actions": prev_actions,
                 "prev_rewards": prev_rewards,
+                "is_training": self._get_is_training_placeholder(),
             },
             observation_space,
             logit_dim,

python/ray/rllib/agents/pg/pg_policy_graph.py

@@ -35,7 +35,8 @@ def __init__(self, obs_space, action_space, config):
         self.model = ModelCatalog.get_model({
             "obs": obs,
             "prev_actions": prev_actions,
-            "prev_rewards": prev_rewards
+            "prev_rewards": prev_rewards,
+            "is_training": self._get_is_training_placeholder(),
         }, obs_space, self.logit_dim, self.config["model"])
         action_dist = dist_class(self.model.outputs)  # logit for each action
 

python/ray/rllib/agents/ppo/ppo.py

@@ -24,7 +24,7 @@
     "sample_batch_size": 200,
     # Number of timesteps collected for each SGD round
     "train_batch_size": 4000,
-    # Total SGD batch size across all devices for SGD (multi-gpu only)
+    # Total SGD batch size across all devices for SGD
     "sgd_minibatch_size": 128,
     # Number of SGD iterations in each outer loop
     "num_sgd_iter": 30,
@@ -49,7 +49,8 @@
     "batch_mode": "truncate_episodes",
     # Which observation filter to apply to the observation
     "observation_filter": "MeanStdFilter",
-    # Use the sync samples optimizer instead of the multi-gpu one
+    # Uses the sync samples optimizer instead of the multi-gpu one. This does
+    # not support minibatches.
     "simple_optimizer": False,
 })
 # __sphinx_doc_end__

python/ray/rllib/agents/ppo/ppo_policy_graph.py

@@ -158,7 +158,8 @@ def __init__(self,
             {
                 "obs": obs_ph,
                 "prev_actions": prev_actions_ph,
-                "prev_rewards": prev_rewards_ph
+                "prev_rewards": prev_rewards_ph,
+                "is_training": self._get_is_training_placeholder(),
             },
             observation_space,
             logit_dim,
@@ -191,7 +192,8 @@ def __init__(self,
                     self.value_function = ModelCatalog.get_model({
                         "obs": obs_ph,
                         "prev_actions": prev_actions_ph,
-                        "prev_rewards": prev_rewards_ph
+                        "prev_rewards": prev_rewards_ph,
+                        "is_training": self._get_is_training_placeholder(),
                     }, observation_space, 1, vf_config).outputs
                     self.value_function = tf.reshape(self.value_function, [-1])
         else:

python/ray/rllib/evaluation/policy_graph.py

@@ -42,7 +42,6 @@ def compute_actions(self,
                         state_batches,
                         prev_action_batch=None,
                         prev_reward_batch=None,
-                        is_training=False,
                         episodes=None):
         """Compute actions for the current policy.
 
@@ -51,7 +50,6 @@ def compute_actions(self,
             state_batches (list): list of RNN state input batches, if any
             prev_action_batch (np.ndarray): batch of previous action values
             prev_reward_batch (np.ndarray): batch of previous rewards
-            is_training (bool): whether we are training the policy
             episodes (list): MultiAgentEpisode for each obs in obs_batch.
                 This provides access to all of the internal episode state,
                 which may be useful for model-based or multiagent algorithms.
@@ -71,7 +69,6 @@ def compute_single_action(self,
                               state,
                               prev_action_batch=None,
                               prev_reward_batch=None,
-                              is_training=False,
                               episode=None):
         """Unbatched version of compute_actions.
 
@@ -80,7 +77,6 @@ def compute_single_action(self,
             state_batches (list): list of RNN state inputs, if any
             prev_action_batch (np.ndarray): batch of previous action values
             prev_reward_batch (np.ndarray): batch of previous rewards
-            is_training (bool): whether we are training the policy
             episode (MultiAgentEpisode): this provides access to all of the
                 internal episode state, which may be useful for model-based or
                 multi-agent algorithms.
@@ -92,7 +88,7 @@ def compute_single_action(self,
         """
 
         [action], state_out, info = self.compute_actions(
-            [obs], [[s] for s in state], is_training, episodes=[episode])
+            [obs], [[s] for s in state], episodes=[episode])
         return action, [s[0] for s in state_out], \
             {k: v[0] for k, v in info.items()}
 

python/ray/rllib/evaluation/sampler.py

@@ -436,15 +436,13 @@ def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
                 builder, [t.obs for t in eval_data],
                 rnn_in_cols,
                 prev_action_batch=[t.prev_action for t in eval_data],
-                prev_reward_batch=[t.prev_reward for t in eval_data],
-                is_training=True)
+                prev_reward_batch=[t.prev_reward for t in eval_data])
         else:
             eval_results[policy_id] = policy.compute_actions(
                 [t.obs for t in eval_data],
                 rnn_in_cols,
                 prev_action_batch=[t.prev_action for t in eval_data],
                 prev_reward_batch=[t.prev_reward for t in eval_data],
-                is_training=True,
                 episodes=[active_episodes[t.env_id] for t in eval_data])
     if builder:
         for k, v in pending_fetches.items():