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https://github.com/Farama-Foundation/Gymnasium.git
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e913bc81b8
* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import time
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from collections import deque
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from typing import Optional
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import numpy as np
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import gym
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class RecordEpisodeStatistics(gym.Wrapper):
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def __init__(self, env, deque_size=100):
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super().__init__(env)
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self.num_envs = getattr(env, "num_envs", 1)
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self.t0 = time.perf_counter()
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self.episode_count = 0
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self.episode_returns = None
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self.episode_lengths = None
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self.return_queue = deque(maxlen=deque_size)
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self.length_queue = deque(maxlen=deque_size)
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self.is_vector_env = getattr(env, "is_vector_env", False)
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def reset(self, **kwargs):
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observations = super().reset(**kwargs)
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self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
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self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
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return observations
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def step(self, action):
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observations, rewards, dones, infos = super().step(action)
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self.episode_returns += rewards
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self.episode_lengths += 1
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if not self.is_vector_env:
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infos = [infos]
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dones = [dones]
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else:
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infos = list(infos) # Convert infos to mutable type
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for i in range(len(dones)):
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if dones[i]:
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infos[i] = infos[i].copy()
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episode_return = self.episode_returns[i]
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episode_length = self.episode_lengths[i]
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episode_info = {
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"r": episode_return,
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"l": episode_length,
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"t": round(time.perf_counter() - self.t0, 6),
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}
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infos[i]["episode"] = episode_info
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self.return_queue.append(episode_return)
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self.length_queue.append(episode_length)
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self.episode_count += 1
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self.episode_returns[i] = 0
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self.episode_lengths[i] = 0
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if self.is_vector_env:
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infos = tuple(infos)
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return (
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observations,
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rewards,
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dones if self.is_vector_env else dones[0],
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infos if self.is_vector_env else infos[0],
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)
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