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import time
from collections import deque
import gym
import numpy as np
class RecordEpisodeStatistics(gym.Wrapper):
def __init__(self, env, deque_size=100):
super().__init__(env)
self.num_envs = getattr(env, "num_envs", 1)
self.n_traj = env.n_traj
self.t0 = time.perf_counter()
self.episode_count = 0
self.episode_returns = None
self.episode_lengths = None
self.return_queue = deque(maxlen=deque_size)
self.length_queue = deque(maxlen=deque_size)
self.is_vector_env = getattr(env, "is_vector_env", False)
def reset(self, **kwargs):
observations = super().reset(**kwargs)
self.episode_returns = np.zeros((self.num_envs, self.n_traj), dtype=np.float32)
self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
self.finished = [False] * self.num_envs
return observations
def step(self, action):
observations, rewards, dones, infos = super().step(action)
self.episode_returns += rewards
self.episode_lengths += 1
if not self.is_vector_env:
infos = [infos]
dones = [dones]
else:
infos = list(infos) # Convert infos to mutable type
for i in range(len(dones)):
if dones[i].all() and not self.finished[i]:
infos[i] = infos[i].copy()
episode_return = self.episode_returns[i]
episode_length = self.episode_lengths[i]
episode_info = {
"r": episode_return.copy(),
"l": episode_length,
"t": round(time.perf_counter() - self.t0, 6),
}
infos[i]["episode"] = episode_info
self.return_queue.append(episode_return)
self.length_queue.append(episode_length)
self.episode_count += 1
self.episode_returns[i] = 0
self.episode_lengths[i] = 0
self.finished[i] = True
if self.is_vector_env:
infos = tuple(infos)
return (
observations,
rewards,
dones if self.is_vector_env else dones[0],
infos if self.is_vector_env else infos[0],
)
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