fixed issue with wrong output layer variable names in ddpg (openai#733)

yueyedeai · Nov 27, 2018 · 25ecb64 · 25ecb64
1 parent 7dc6bc7
commit 25ecb64
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diff --git a/baselines/ddpg/ddpg_learner.py b/baselines/ddpg/ddpg_learner.py
@@ -185,7 +185,7 @@ def setup_critic_optimizer(self):
         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 'kernel' in var.name and 'output' not in var.name]
+            critic_reg_vars = [var for var in self.critic.trainable_vars if var.name.endswith('/w:0') 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))

diff --git a/baselines/ddpg/models.py b/baselines/ddpg/models.py
@@ -42,7 +42,7 @@ def __call__(self, obs, action, reuse=False):
         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))
+            x = tf.layers.dense(x, 1, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3), name='output')
         return x
 
     @property