3592f45446
- Fixes an inconsistency in the implementation of Discrete CRR. Now it uses `Critic` class for its critic, following conventions in other actor-critic policies; - Updates several offline policies to use `ActorCritic` class for its optimizer to eliminate randomness caused by parameter sharing between actor and critic; - Add `writer.flush()` in TensorboardLogger to ensure real-time result; - Enable `test_collector=None` in 3 trainers to turn off testing during training; - Updates the Atari offline results in README.md; - Moves Atari offline RL examples to `examples/offline`; tests to `test/offline` per review comments.
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
import time
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from typing import Any, Callable, Dict, Optional, Union
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import numpy as np
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from tianshou.data import Collector
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from tianshou.policy import BasePolicy
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from tianshou.utils import BaseLogger
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def test_episode(
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policy: BasePolicy,
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collector: Collector,
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test_fn: Optional[Callable[[int, Optional[int]], None]],
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epoch: int,
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n_episode: int,
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logger: Optional[BaseLogger] = None,
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global_step: Optional[int] = None,
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reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
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) -> Dict[str, Any]:
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"""A simple wrapper of testing policy in collector."""
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collector.reset_env()
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collector.reset_buffer()
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policy.eval()
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if test_fn:
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test_fn(epoch, global_step)
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result = collector.collect(n_episode=n_episode)
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if reward_metric:
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rew = reward_metric(result["rews"])
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result.update(rews=rew, rew=rew.mean(), rew_std=rew.std())
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if logger and global_step is not None:
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logger.log_test_data(result, global_step)
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return result
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def gather_info(
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start_time: float,
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train_c: Optional[Collector],
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test_c: Optional[Collector],
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best_reward: float,
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best_reward_std: float,
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) -> Dict[str, Union[float, str]]:
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"""A simple wrapper of gathering information from collectors.
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:return: A dictionary with the following keys:
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* ``train_step`` the total collected step of training collector;
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* ``train_episode`` the total collected episode of training collector;
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* ``train_time/collector`` the time for collecting transitions in the \
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training collector;
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* ``train_time/model`` the time for training models;
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* ``train_speed`` the speed of training (env_step per second);
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* ``test_step`` the total collected step of test collector;
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* ``test_episode`` the total collected episode of test collector;
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* ``test_time`` the time for testing;
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* ``test_speed`` the speed of testing (env_step per second);
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* ``best_reward`` the best reward over the test results;
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* ``duration`` the total elapsed time.
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"""
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duration = time.time() - start_time
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model_time = duration
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result: Dict[str, Union[float, str]] = {
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"duration": f"{duration:.2f}s",
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"train_time/model": f"{model_time:.2f}s",
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}
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if test_c is not None:
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model_time = duration - test_c.collect_time
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test_speed = test_c.collect_step / test_c.collect_time
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result.update(
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{
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"test_step": test_c.collect_step,
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"test_episode": test_c.collect_episode,
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"test_time": f"{test_c.collect_time:.2f}s",
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"test_speed": f"{test_speed:.2f} step/s",
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"best_reward": best_reward,
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"best_result": f"{best_reward:.2f} ± {best_reward_std:.2f}",
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"duration": f"{duration:.2f}s",
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"train_time/model": f"{model_time:.2f}s",
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}
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)
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if train_c is not None:
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model_time -= train_c.collect_time
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if test_c is not None:
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train_speed = train_c.collect_step / (duration - test_c.collect_time)
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else:
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train_speed = train_c.collect_step / duration
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result.update(
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{
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"train_step": train_c.collect_step,
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"train_episode": train_c.collect_episode,
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"train_time/collector": f"{train_c.collect_time:.2f}s",
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"train_time/model": f"{model_time:.2f}s",
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"train_speed": f"{train_speed:.2f} step/s",
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}
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)
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return result
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