Fix some typing issues with curiosity

ml-agents/mlagents/trainers/components/reward_signals/curiosity/model.py

 
                 # Create the encoder ops for current and next visual input.
                 # Note that these encoders are siamese.
-                encoded_visual = self.policy_model.create_visual_observation_encoder(
+                encoded_visual = LearningModel.create_visual_observation_encoder(
                     self.policy_model.visual_in[i],
                     self.encoding_size,
                     LearningModel.swish,
                 )
 
-                encoded_next_visual = self.policy_model.create_visual_observation_encoder(
+                encoded_next_visual = LearningModel.create_visual_observation_encoder(
                     self.next_visual_in[i],
                     self.encoding_size,
                     LearningModel.swish,
                 name="curiosity_next_vector_observation",
             )
 
-            encoded_vector_obs = self.policy_model.create_vector_observation_encoder(
+            encoded_vector_obs = LearningModel.create_vector_observation_encoder(
                 self.policy_model.vector_in,
                 self.encoding_size,
                 LearningModel.swish,
             )
-            encoded_next_vector_obs = self.policy_model.create_vector_observation_encoder(
+            encoded_next_vector_obs = LearningModel.create_vector_observation_encoder(
                 self.next_vector_in,
                 self.encoding_size,
                 LearningModel.swish,

ml-agents/mlagents/trainers/components/reward_signals/curiosity/signal.py

         :return: Feed_dict needed for update.
         """
         feed_dict = {
-            policy.batch_size: num_sequences,
+            policy.batch_size_ph: num_sequences,
             policy.sequence_length: self.policy.sequence_length,
             policy.mask_input: mini_batch["masks"],
         }