a2c-HalfCheetahBulletEnv-v0 / wrappers /vec_episode_recorder.py
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A2C playing HalfCheetahBulletEnv-v0 from https://github.com/sgoodfriend/rl-algo-impls/tree/0760ef7d52b17f30219a27c18ba52c8895025ae3
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import numpy as np
from gym.wrappers.monitoring.video_recorder import VideoRecorder
from wrappers.vectorable_wrapper import (
VecotarableWrapper,
VecEnvObs,
VecEnvStepReturn,
)
class VecEpisodeRecorder(VecotarableWrapper):
def __init__(self, env, base_path: str, max_video_length: int = 3600):
super().__init__(env)
self.base_path = base_path
self.max_video_length = max_video_length
self.video_recorder = None
self.recorded_frames = 0
def step(self, actions: np.ndarray) -> VecEnvStepReturn:
obs, rew, dones, infos = self.env.step(actions)
# Using first env to record episodes
if self.video_recorder:
self.video_recorder.capture_frame()
self.recorded_frames += 1
if dones[0] and infos[0].get("episode"):
episode_info = {
k: v.item() if hasattr(v, "item") else v
for k, v in infos[0]["episode"].items()
}
self.video_recorder.metadata["episode"] = episode_info
if dones[0] or self.recorded_frames > self.max_video_length:
self._close_video_recorder()
return obs, rew, dones, infos
def reset(self) -> VecEnvObs:
obs = self.env.reset()
self._start_video_recorder()
return obs
def _start_video_recorder(self) -> None:
self._close_video_recorder()
self.video_recorder = VideoRecorder(
self.env,
base_path=self.base_path,
)
self.video_recorder.capture_frame()
self.recorded_frames = 1
def _close_video_recorder(self) -> None:
if self.video_recorder:
self.video_recorder.close()
self.video_recorder = None