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import gym
import numpy as np
from gym.wrappers.monitoring.video_recorder import VideoRecorder
from typing import Tuple, Union
ObsType = Union[np.ndarray, dict]
ActType = Union[int, float, np.ndarray, dict]
class EpisodeRecordVideo(gym.Wrapper):
def __init__(
self,
env: gym.Env,
video_path_prefix: str,
step_increment: int = 1,
video_step_interval: int = 1_000_000,
max_video_length: int = 3600,
) -> None:
super().__init__(env)
self.video_path_prefix = video_path_prefix
self.step_increment = step_increment
self.video_step_interval = video_step_interval
self.max_video_length = max_video_length
self.total_steps = 0
self.next_record_video_step = 0
self.video_recorder = None
self.recorded_frames = 0
def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
obs, rew, done, info = self.env.step(action)
self.total_steps += self.step_increment
# Using first env to record episodes
if self.video_recorder:
self.video_recorder.capture_frame()
self.recorded_frames += 1
if info.get("episode"):
episode_info = {
k: v.item() if hasattr(v, "item") else v
for k, v in info["episode"].items()
}
self.video_recorder.metadata["episode"] = episode_info
if self.recorded_frames > self.max_video_length:
self._close_video_recorder()
return obs, rew, done, info
def reset(self, **kwargs) -> ObsType:
obs = self.env.reset(**kwargs)
if self.video_recorder:
self._close_video_recorder()
elif self.total_steps >= self.next_record_video_step:
self._start_video_recorder()
return obs
def _start_video_recorder(self) -> None:
self._close_video_recorder()
video_path = f"{self.video_path_prefix}-{self.next_record_video_step}"
self.video_recorder = VideoRecorder(
self.env,
base_path=video_path,
metadata={"step": self.total_steps},
)
self.video_recorder.capture_frame()
self.recorded_frames = 1
self.next_record_video_step += self.video_step_interval
def _close_video_recorder(self) -> None:
if self.video_recorder:
self.video_recorder.close()
self.video_recorder = None
self.recorded_frames = 0
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