From 7b08db9f8cd85d185879e5bef778e8855f2a06cf Mon Sep 17 00:00:00 2001 From: Sven Mika Date: Thu, 2 Apr 2020 03:03:14 +0200 Subject: [PATCH] [RLlib] Remove all instances of tf.contrib.layers. ... from RLlib code (deprecated). (#7851) --- rllib/agents/ddpg/ddpg_policy.py | 13 ++++--------- rllib/agents/dqn/distributional_q_model.py | 8 +++----- 2 files changed, 7 insertions(+), 14 deletions(-) diff --git a/rllib/agents/ddpg/ddpg_policy.py b/rllib/agents/ddpg/ddpg_policy.py index f0d54b4a6..641f34284 100644 --- a/rllib/agents/ddpg/ddpg_policy.py +++ b/rllib/agents/ddpg/ddpg_policy.py @@ -54,6 +54,7 @@ class DDPGPostprocessing: feed_dict={ self.cur_observations: states, self._is_exploring: False, + self._timestep: self.global_timestep, }) distance_in_action_space = np.sqrt( np.mean(np.square(clean_actions - noisy_actions))) @@ -414,16 +415,10 @@ class DDPGTFPolicy(DDPGPostprocessing, TFPolicy): activation = getattr(tf.nn, self.config["actor_hidden_activation"]) for hidden in self.config["actor_hiddens"]: + action_out = tf.layers.dense( + action_out, units=hidden, activation=activation) if self.config["parameter_noise"]: - import tensorflow.contrib.layers as layers - action_out = layers.fully_connected( - action_out, - num_outputs=hidden, - activation_fn=activation, - normalizer_fn=layers.layer_norm) - else: - action_out = tf.layers.dense( - action_out, units=hidden, activation=activation) + action_out = tf.keras.layers.LayerNormalization()(action_out) action_out = tf.layers.dense( action_out, units=action_space.shape[0], activation=None) diff --git a/rllib/agents/dqn/distributional_q_model.py b/rllib/agents/dqn/distributional_q_model.py index b331317b0..dd981412b 100644 --- a/rllib/agents/dqn/distributional_q_model.py +++ b/rllib/agents/dqn/distributional_q_model.py @@ -125,14 +125,12 @@ class DistributionalQModel(TFModelV2): state_out = self._noisy_layer("dueling_hidden_%d" % i, state_out, q_hiddens[i], sigma0) - elif parameter_noise: - state_out = tf.keras.layers.Dense( - units=q_hiddens[i], - activation_fn=tf.nn.relu, - normalizer_fn=tf.contrib.layers.layer_norm)(state_out) else: state_out = tf.keras.layers.Dense( units=q_hiddens[i], activation=tf.nn.relu)(state_out) + if parameter_noise: + state_out = tf.keras.layers.LayerNormalization()( + state_out) if use_noisy: state_score = self._noisy_layer( "dueling_output",