Gymnasium/docs/wrappers.md

Gym includes numerous wrappers for environments that include preprocessing and various other functions. The following categories are included:

Observation Wrappers

DelayObservation(env, delay) [text]

• Bring over from SuperSuit

FilterObservation(env, filter_keys) [text]

• Needs review (including for good assertion messages and test coverage)

FlattenObservation(env) [text]

• Needs good asseration messages

FrameStack(env, num_stack, lz4_compress=False) [text]

• Needs review (including for good assertion messages and test coverage)

GrayScaleObservation(env) [text]

• Needs R/G/B channel argument added like supersuit wrapper
• Needs review (including for good assertion messages and test coverage)
• Needs CV2 dependency replaced with SuperSuit's way of doing full grey scaling: https://github.com/PettingZoo-Team/SuperSuit/blob/master/supersuit/utils/basic_transforms/color_reduction.py

PixelObservationWrapper(pixels_only=True, render_kwargs=None, pixel_keys=("pixels",)) [text]

• Needs review (including for good assertion messages and test coverage)

RescaleObservation(env, min_obs, max_obs) [text]

• Bring over from Supersuit or from https://github.com/openai/gym/pull/1635

ResizeObservation(env, shape) [text]

• Needs review (including for good assertion messages and test coverage)
• Switch from CV2 to Lycon2 once released

TimeAwareObservation(env) [text]

• Needs review (including for good assertion messages and test coverage)

Action Wrappers

ClipAction(env) [text]

• Needs review (including for good assertion messages and test coverage)

ChangeDtype(env, dtype) [text]

• Bring over from SuperSuit

RescaleAction(env, min_action, max_action) [text]

• Needs review (including for good assertion messages and test coverage)

StickyActions(env, repeat_action_probability) [text]

• Create as separate wrapper from Atari/bring over from SuperSuit

Reward Wrappers

ClipReward(env, lower_bound-1, upper_bound=1) [text]

• Bring over from SuperSuit

Lambda Wrappers

ActionLambda(env, change_action_fn, change_space_fn) [text]

• Bring over from SuperSuit

ObservationLambda(env, observation_fn, observation_space_fn) [text]

• Bring over from SuperSuit, replaces TransformObservation

RewardLambda(env, reward_fn) [text]

• Bring over from SuperSuit, replaces TransformReward

Other

AtariPreprocessing(env, noop_max=30, frame_skip=4, screen_size=84, terminal_on_life_loss=False, grayscale_obs=True, grayscale_newaxis=False, scale_obs=False) [text]

• Needs review (including for good assertion messages and test coverage)

RecordEpisodeStatistic(env) [text]

• Needs review (including for good assertion messages and test coverage)

RecordVideo(env, video_folder, record_video_trigger, video_length=0, name_prefix="rl-video") [text]

The RecordVideo is a lightweight gym.Wrapper that helps recording videos. See the following code as an example.

import gym
env = gym.make("CartPole-v1")
env = gym.wrappers.RecordVideo(env, "videos", record_video_trigger=lambda x: x % 100 == 0)
observation = env.reset()
for _ in range(1000):
  env.render()
  action = env.action_space.sample() # your agent here (this takes random actions)
  observation, reward, done, info = env.step(action)

  if done:
    observation = env.reset()
env.close()

To use it, you need to specify the video_folder as the storing location and record_video_trigger as a frequency at which you want to record.

There are two modes of video the recording:

1. Episodic mode.
   • By default video_length=0 means the wrapper will record episodic videos: it will keep record the frames until the env returns done=True.
2. Fixed-interval mode.
   • By tuning video_length such as video_length=100, the wrapper will record exactly 100 frames for every videos the wrapper creates.

Lastly the name_prefix allows you to customize the name of the videos.

TimeLimit(env, max_episode_steps) [text]

• Needs review (including for good assertion messages and test coverage)

Some sort of vector environment conversion wrapper needs to be added here, this will be figured out after the API is changed.