Alternative methods for deprecated gym.wrappers.Monitor() - openai-gym

Question

The example is from Chapter 8 of Grokking Deep Reinforcement Learning written by Miguel Morales.
Please click here
The wrappers.Monitor is deprecated after the book is published. The code in question is as below:
env = wrappers.Monitor(
env, mdir, force=True,
mode=monitor_mode,
video_callable=lambda e_idx: record) if monitor_mode else env
I searched the internet and tried 2 methods but failed.
1- gnwrapper.Monitor
I pip install gym-notebook-wrapper and import gnwrapper, then rewrite the code
env = gnwrapper.Monitor(gym.make(env_name),directory="./")
A [WinError 2] The system cannot find the file specified error message is returned.
2- gym.wrappers.RecordVideo
I from gym.wrappers import RecordVideo then rewrite the code
env = RecordVideo(gym.make(env_name), "./")
A AttributeError: 'CartPoleEnv' object has no attribute 'videos' error message is returned.
Is there any way to solve the problem?

Answer 1

After some experiments, the best way to work around the problem is to downgrade the gym version to 0.20.0, in order to preserve the wrappers.Monitor function.
Moreover, the subprocess.Popen and subprocess.check_output does not work as the original author suggested, so I use moviepy (please pip install moviepy if you do not have this libarary and from moviepy.editor import VideoFileClip at the very beginning) to change MP4 to GIF files. The code in get_gif_html is amended as follows.
gif_path = basename + '.gif'
if not os.path.exists(gif_path):
videoClip = VideoFileClip(video_path)
videoClip.write_gif(gif_path, logger=None)
gif = io.open(gif_path, 'r+b').read()
The program now completely works.
Edit (24 Jul 2022):
The following solution is for gym version 0.25.0, as I hope the program works for later versions. Windows 10 and Juptyer Notebook is used for the demonstration.
(1) Maintain the moviepy improvement stated above
(2) import from gym.wrappers.record_video import RecordVideo and substitute
env = wrappers.Monitor(
env, mdir, force=True,
mode=monitor_mode,
video_callable=lambda e_idx: record) if monitor_mode else
with this
if monitor_mode:
env = RecordVideo(env, mdir, episode_trigger=lambda e_idx:record)
env.reset()
env.start_video_recorder()
else:
env = env
RecordVideo function takes (env, video_folder, episode_trigger) arguments, and video_callable and episode_trigger are the same thing.
(3) Loss of env.videos in get_gif_html function in new versions.
In the old setting, after the video is recorded, the MP4 and meta.json files are stored in the following address with a random file name
C:\Users\xxx\AppData\Local\Temp
https://i.stack.imgur.com/Za3Sd.jpg
This env.videos function returns a list of tuples, each tuples consisted of address path of MP4 and meta.json.
I tried to recreate the env.videos by
(a) declare the mdir as global variable
def get_make_env_fn(**kargs):
def make_env_fn(env_name, seed=None, render=None, record=False,
unwrapped=False, monitor_mode=None,
inner_wrappers=None, outer_wrappers=None):
global mdir
mdir = tempfile.mkdtemp()
(b) in the demo_progression and demo_last function, grouping all MP4 and meta_json files, zipping them and use list comprehension to make a new env_videos variable (as opposed to env.videos)
env.close()
mp4_files = ([(mdir + '\\' + file) for file in os.listdir(mdir) if file.endswith('.mp4')])
meta_json_files = ([(mdir + '\\' + file) for file in os.listdir(mdir) if file.endswith('.meta.json')])
env_videos = [(mp4, meta_json) for mp4, meta_json in zip(mp4_files, meta_json_files)]
data = get_gif_html(env_videos=env_videos,
These are all the changes.