mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-28 01:07:11 +00:00
925823661d
* First find/replace, now tests * Fixes to the vector env * Make seed keyword only in wrappers * (try to) fix the bug with old environments using new wrappers (with the seed keyword) * black * Change **kwargs to options, try to make it work; black * Add OrderEnforcing wrapper to wrapper exports Add a test for compatibility with old (pybullet-like) envs * Add OrderEnforcing wrapper to wrapper exports Add a test for compatibility with old (pybullet-like) envs black * Update the env checker * Update the env checker * Update the env checker to use inspect (might fail tests, let's see) * Allow the signature to include kwargs in env_checker * Minor fix
56 lines
1.9 KiB
Python
56 lines
1.9 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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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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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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