Add the deep dyna-q agent #322
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| ) | ||
| batch = self.preprocess_update_batch(batch) | ||
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| self._model_optimizer.zero_grad() |
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I think this can be moved down to right above line 435
| :py:class:`~hive.replays.circular_replay.CircularReplayBuffer`. | ||
| discount_rate (float): A number between 0 and 1 specifying how much | ||
| future rewards are discounted by the agent. | ||
| n_step (int): The horizon used in n-step returns to compute TD(n) targets. |
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Doubt: Is the length of the horizon while planning to tune the policy?
| stack_size=stack_size, | ||
| gamma=discount_rate, | ||
| ) | ||
| self._planning_buffer = planning_buffer( |
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Doubt: Why are there separate replay buffers for planning and learning?
| ): | ||
| self._logger.log_scalar("train_qval", torch.max(qvals), self._timescale) | ||
| agent_traj_state = {} | ||
| return action, agent_traj_state |
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Minor comment: Defining agent_traj_state might not be necessary.
| "observation": update_info["observation"], | ||
| "action": update_info["action"], | ||
| "reward": update_info["reward"], | ||
| "done": update_info["terminated"], |
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Why "or update_info["truncated"]" in not added for this replay buffer?
| return | ||
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| preprocessed_learning_update_info, |
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Why have 2 replay buffers? From what I understood, both replay buffers are storing the same transitions. It's just that the batch_size for planning and model learning might change. But that can be passed as a separate instead. Also, having 2 buffers increases the memory required by the model.
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| # Observations | ||
| obs_pred_list = [] | ||
| for a in range(self._act_dim): |
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Curious question: Isn't there a better way to do it without the for loop?
| # Observations | ||
| self._obs_encoder = observation_encoder_net(in_dim) | ||
| obs_predictor_in_dim = ( | ||
| np.prod(calculate_output_dim(self._obs_encoder, in_dim)) + 1 |
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Question: Is the dimension 1 added for the action? I thought the actions are one-hot in general for discrete action spaces.
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