Fix test

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

         super()._process_trajectory(trajectory)
         agent_id = trajectory.agent_id  # All the agents should have the same ID
 
-        # Add to episode_steps
-        self.episode_steps[agent_id] += len(trajectory.steps)
-
         agent_buffer_trajectory = trajectory.to_agentbuffer()
         # Update the normalization
         if self.is_training:

ml-agents/mlagents/trainers/rl_trainer.py

             "environment": defaultdict(lambda: 0)
         }
         self.update_buffer: AgentBuffer = AgentBuffer()
-        self.episode_steps: Dict[str, int] = defaultdict(lambda: 0)
         # Write hyperparameters to Tensorboard
         if self.is_training:
             self.write_tensorboard_text("Hyperparameters", self.trainer_parameters)

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

         last_step = trajectory.steps[-1]
         agent_id = trajectory.agent_id  # All the agents should have the same ID
 
-        # Add to episode_steps
-        self.episode_steps[agent_id] += len(trajectory.steps)
-
         agent_buffer_trajectory = trajectory.to_agentbuffer()
 
         # Update the normalization

ml-agents/mlagents/trainers/tests/test_rl_trainer.py

 def test_rl_trainer():
     trainer = create_rl_trainer()
     agent_id = "0"
-    trainer.episode_steps[agent_id] = 3
-    for agent_id in trainer.episode_steps:
-        assert trainer.episode_steps[agent_id] == 0
     for rewards in trainer.collected_rewards.values():
         for agent_id in rewards:
             assert rewards[agent_id] == 0