Clean up some stuff

ml-agents/mlagents/trainers/agent_processor.py

         self.processing_buffer[agent_id]["actions"].append(actions[agent_idx])
         self.processing_buffer[agent_id]["action_probs"].append(a_dist[agent_idx])
 
-    def process_experiences(self):
-        pass
-
 
 class ProcessingBuffer(dict):
     """

ml-agents/mlagents/trainers/ppo/trainer.py

         Takes a trajectory and processes it, putting it into the update buffer.
         Processing involves calculating value and advantage targets for model updating step.
         """
-        agent_id = trajectory.steps[-1].agent_id
+        agent_id = trajectory.steps[
+            -1
+        ].agent_id  # All the agents should have the same ID
         agent_buffer_trajectory = self._trajectory_to_agentbuffer(trajectory)
         # Update the normalization
         if self.is_training: