mirror of
https://github.com/saymrwulf/stable-baselines3.git
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daaebd0a52
* Drop python 3.8 support, add python 3.12 support * Upgrade to python 3.9 syntax * Fixes for Numpy v2 * Fix doc warning
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
import time
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import warnings
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from typing import Optional
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import numpy as np
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from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn, VecEnvWrapper
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class VecMonitor(VecEnvWrapper):
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"""
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A vectorized monitor wrapper for *vectorized* Gym environments,
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it is used to record the episode reward, length, time and other data.
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Some environments like `openai/procgen <https://github.com/openai/procgen>`_
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or `gym3 <https://github.com/openai/gym3>`_ directly initialize the
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vectorized environments, without giving us a chance to use the ``Monitor``
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wrapper. So this class simply does the job of the ``Monitor`` wrapper on
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a vectorized level.
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:param venv: The vectorized environment
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:param filename: the location to save a log file, can be None for no log
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:param info_keywords: extra information to log, from the information return of env.step()
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"""
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def __init__(
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self,
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venv: VecEnv,
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filename: Optional[str] = None,
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info_keywords: tuple[str, ...] = (),
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):
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# Avoid circular import
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from stable_baselines3.common.monitor import Monitor, ResultsWriter
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# This check is not valid for special `VecEnv`
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# like the ones created by Procgen, that does follow completely
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# the `VecEnv` interface
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try:
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is_wrapped_with_monitor = venv.env_is_wrapped(Monitor)[0]
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except AttributeError:
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is_wrapped_with_monitor = False
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if is_wrapped_with_monitor:
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warnings.warn(
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"The environment is already wrapped with a `Monitor` wrapper"
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"but you are wrapping it with a `VecMonitor` wrapper, the `Monitor` statistics will be"
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"overwritten by the `VecMonitor` ones.",
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UserWarning,
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)
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VecEnvWrapper.__init__(self, venv)
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self.episode_count = 0
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self.t_start = time.time()
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env_id = None
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if hasattr(venv, "spec") and venv.spec is not None:
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env_id = venv.spec.id
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self.results_writer: Optional[ResultsWriter] = None
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if filename:
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self.results_writer = ResultsWriter(
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filename, header={"t_start": self.t_start, "env_id": str(env_id)}, extra_keys=info_keywords
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)
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self.info_keywords = info_keywords
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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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def reset(self) -> VecEnvObs:
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obs = self.venv.reset()
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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 obs
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def step_wait(self) -> VecEnvStepReturn:
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obs, rewards, dones, infos = self.venv.step_wait()
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self.episode_returns += rewards
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self.episode_lengths += 1
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new_infos = list(infos[:])
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for i in range(len(dones)):
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if dones[i]:
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info = 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 = {"r": episode_return, "l": episode_length, "t": round(time.time() - self.t_start, 6)}
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for key in self.info_keywords:
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episode_info[key] = info[key]
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info["episode"] = episode_info
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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.results_writer:
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self.results_writer.write_row(episode_info)
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new_infos[i] = info
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return obs, rewards, dones, new_infos
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def close(self) -> None:
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if self.results_writer:
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self.results_writer.close()
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return self.venv.close()
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