DQN playing LunarLander-v2 from https://github.com/sgoodfriend/rl-algo-impls/tree/0511de345b17175b7cf1ea706c3e05981f11761c
5c9c091
| from collections import deque | |
| from typing import Any, Dict, List, Optional | |
| import numpy as np | |
| from torch.utils.tensorboard.writer import SummaryWriter | |
| from rl_algo_impls.shared.stats import Episode, EpisodesStats | |
| from rl_algo_impls.wrappers.vectorable_wrapper import ( | |
| VecEnvObs, | |
| VecEnvStepReturn, | |
| VecotarableWrapper, | |
| ) | |
| class EpisodeStatsWriter(VecotarableWrapper): | |
| def __init__( | |
| self, | |
| env, | |
| tb_writer: SummaryWriter, | |
| training: bool = True, | |
| rolling_length=100, | |
| additional_keys_to_log: Optional[List[str]] = None, | |
| ): | |
| super().__init__(env) | |
| self.training = training | |
| self.tb_writer = tb_writer | |
| self.rolling_length = rolling_length | |
| self.episodes = deque(maxlen=rolling_length) | |
| self.total_steps = 0 | |
| self.episode_cnt = 0 | |
| self.last_episode_cnt_print = 0 | |
| self.additional_keys_to_log = ( | |
| additional_keys_to_log if additional_keys_to_log is not None else [] | |
| ) | |
| def step(self, actions: np.ndarray) -> VecEnvStepReturn: | |
| obs, rews, dones, infos = self.env.step(actions) | |
| self._record_stats(infos) | |
| return obs, rews, dones, infos | |
| # Support for stable_baselines3.common.vec_env.VecEnvWrapper | |
| def step_wait(self) -> VecEnvStepReturn: | |
| obs, rews, dones, infos = self.env.step_wait() | |
| self._record_stats(infos) | |
| return obs, rews, dones, infos | |
| def _record_stats(self, infos: List[Dict[str, Any]]) -> None: | |
| self.total_steps += getattr(self.env, "num_envs", 1) | |
| step_episodes = [] | |
| for info in infos: | |
| ep_info = info.get("episode") | |
| if ep_info: | |
| additional_info = {k: info[k] for k in self.additional_keys_to_log} | |
| episode = Episode(ep_info["r"], ep_info["l"], info=additional_info) | |
| step_episodes.append(episode) | |
| self.episodes.append(episode) | |
| if step_episodes: | |
| tag = "train" if self.training else "eval" | |
| step_stats = EpisodesStats(step_episodes, simple=True) | |
| step_stats.write_to_tensorboard(self.tb_writer, tag, self.total_steps) | |
| rolling_stats = EpisodesStats(self.episodes) | |
| rolling_stats.write_to_tensorboard( | |
| self.tb_writer, f"{tag}_rolling", self.total_steps | |
| ) | |
| self.episode_cnt += len(step_episodes) | |
| if self.episode_cnt >= self.last_episode_cnt_print + self.rolling_length: | |
| print( | |
| f"Episode: {self.episode_cnt} | " | |
| f"Steps: {self.total_steps} | " | |
| f"{rolling_stats}" | |
| ) | |
| self.last_episode_cnt_print += self.rolling_length | |
| def reset(self) -> VecEnvObs: | |
| return self.env.reset() | |