5ed6c1c7aa
This PR separates the `global_step` into `env_step` and `gradient_step`. In the future, the data from the collecting state will be stored under `env_step`, and the data from the updating state will be stored under `gradient_step`. Others: - add `rew_std` and `best_result` into the monitor - fix network unbounded in `test/continuous/test_sac_with_il.py` and `examples/box2d/bipedal_hardcore_sac.py` - change the dependency of ray to 1.0.0 since ray-project/ray#10134 has been resolved
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
import time
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from typing import Dict, List, Union, Callable, Optional
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from tianshou.data import Collector
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from tianshou.policy import BasePolicy
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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: Union[int, List[int]],
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writer: Optional[SummaryWriter] = None,
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global_step: Optional[int] = None,
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) -> Dict[str, float]:
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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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if collector.get_env_num() > 1 and isinstance(n_episode, int):
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n = collector.get_env_num()
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n_ = np.zeros(n) + n_episode // n
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n_[:n_episode % n] += 1
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n_episode = list(n_)
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result = collector.collect(n_episode=n_episode)
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if writer is not None and global_step is not None:
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for k in result.keys():
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writer.add_scalar("test/" + k, result[k], global_step=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: Collector,
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test_c: 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 frames 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 (frames 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 (frames 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 - train_c.collect_time - test_c.collect_time
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train_speed = train_c.collect_step / (duration - test_c.collect_time)
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test_speed = test_c.collect_step / test_c.collect_time
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return {
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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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"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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}
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