gym-notebook-wrapper 1.3.3

Wrapper for running/rendering OpenAI Gym on Jupyter Notebook

Gym-Notebook-Wrapper

Gym-Notebook-Wrapper provides small wrappers for running and rendering OpenAI Gym and Brax on Jupyter Notebook or similar (e.g. Google Colab).

1. Requirement

• Linux
• Xvfb (for Gym)
  • On Ubuntu, you can install sudo apt update && sudo apt install xvfb.
• Open GL (for some environment)
  • On Ubuntu, you can install sudo apt update && sudo apt install python3-opengl

2. Installation

You can install from PyPI with pip install gym-notebook-wrapper

3. Rendering Gym

Warning
Gym has changed its API. For example, until v0.25.2 env.step(action) returns 4 values, but from v0.26.0 it returns 5 values. (done was divided to termination and truncation.)

Three classes are implemented in gnwrapper module in this gym-notebook-wrapper package.

3.1 Simple One Shot Animation

Wrap gym.Env class with gnwrapper.Animation. That's all! The render() method shows the environment on its output. An example code is following;

3.1.1 Code

import gnwrapper
import gym

env = gnwrapper.Animation(gym.make('CartPole-v1', render_mode="rgb_array"))

obs = env.reset()

for _ in range(1000):
    next_obs, reward, term, trunc, info = env.step(env.action_space.sample())
    env.render()

    obs = next_obs
    if term or trunc:
        obs = env.reset()

3.1.2 Limitation

• Calling render() method delete the other output for the same cell.
• The output image is shown only once.

3.2 Loop Animation

Wrap gym.Env class with gnwrapper.LoopAnimation. This wrapper stores display image when render() methos is called and shows the loop animation by display(dpi=72,interval=50) methos.

3.2.1 Code

import gnwrapper
import gym

env = gnwrapper.LoopAnimation(gym.make('CartPole-v1', render_mode="rgb_array"))

obs = env.reset()

for _ in range(100):
    next_obs, reward, term, trunc, info = env.step(env.action_space.sample())
    env.render()

    obs = next_obs
    if term or trunc:
        obs = env.reset()

env.display()

3.2.2 Limitation

• Require a lot of memory to store and display large steps of display
  • Can raise memory error

3.3 Movie Animation

Wrap gym.Env class with gnwrapper.Monitor. This wrapper inherits gym.wrappers.Monitor (for gym<=0.19.0) or gym.wrapper.RecordVideo (for gym>=0.20.0), and implements display() method for embedding mp4 movie into Notebook.

Note: gym.wrappers.Monitor was deprecated at gym==0.20.0, which was released on 14th September 2021. See.

If you call display(reset=True), the video list is cleared and the next display() method shows only new videos.

3.3.1 Code

import gnwrapper
import gym

env = gnwrapper.Monitor(gym.make('CartPole-v1', render_mode="rgb_array"),directory="./")

o = env.reset()

for _ in range(100):
    o, r, term, trunc, i = env.step(env.action_space.sample())
    if term or trunc:
        env.reset()

env.display()

3.3.2 Limitation

• Require disk space for save movie

3.4 Notes

gnwrapper.Animation and gnwrapper.LoopAnimation inherit from gym.Wrapper, so that it can access any fields or mothods of gym.Env and gym.Wrapper (e.g. action_space).

4. Rendering Brax

Brax has HTML rendering in brax.io.html. We provide small wrapper classes to record episodes automatically and to display on Jupyter Notebook easily.

Two classes are implemented in gnwrapper.brax module. Since this module requires brax package, the statement import gnwrapper doesn't import gnwrapper.brax submodule. You must explicitly import it by import gnwrapper.brax or from gnwrapper import brax etc.

4.1 HTML Viewer with Brax Native Environment

Wrap brax.envs.Env with gnwrapper.brax.BraxHTML. step() method automatically stores an episode, and saves it as html file at the episode end. You can embeds HTML viewer by calling display() method. Of cource, you can open the html file with your local browser as long as you have internet access. (Data is saved in the html file, however, the viewer is hosted on CDN.)

Since this wrapper has Python side effect, you cannot wrap step() / reset() methods with jax.jit. Insted, you can wrap internal (original) step() / reset() methods by setting jit=True at the wrapper constructor.

4.1.1 Code

from brax import envs
import brax.jumpy as jp

from gnwrapper.brax import BraxHTML

rng = jp.random_prngkey(seed=42)

ant = BraxHTML(envs.create("ant", auto_reset=False), video_callable = lambda ep: True)

for i in range(2):
    rng, rng_use = jp.random_split(rng)
    state = ant.reset(rng_use)

    while True:
        rng, rng_use = jp.random_split(rng)
        state = ant.step(state, jp.random_uniform(rng_use, (ant.action_size,)))
        if state.done:
        # When `state.done = True`, the episode is written at html file.
            break

# We can get list of recorded episodes.
episodes = ant.recorded_episodes()

# `display()` method show all recorded episodes.
# `display(1)` shows only episode 1, if it is recorded
# `display([1, 2])` shows episode 1 & 2, if they are recorded, etc.
ant.display()

4.1.2 Parameters

Argument	Type	Description
env	brax.envs.Env	Environment
directory=None	Optional[str]	Directory to store html. If None(default), time stamp ("%Y%m%d-%H%M%S") is used.
heght=480	int	Viewer height in px. (There is a Brax bug (this issue), however, PR was merged.)
video_callable=None	Optional[Callable[[int], bool]]	Function to determine whether each episode is recorded or not. If None (default), every 1000 and cubic number less than 1000 are recorded
jit=True	bool	Whether step/reset methods will be wapped by jax.jit

4.2 HTML Viewer with Gym compatible Brax Environment

Wrap brax.wrappers.GymWrapper with gnwrapper.brax.GymHTML. step() method automatically stores an episode, and saves it as html file at the episode end. You can embeds HTML viewer by calling display() method. Of cource, you can open the html file with your local browser as long as you have internet access. (Data is saved in the html file, however, the viewer is hosted on CDN.)

Since brax.wrapper.GymWrapper already wraps step() / reset() methods with jax.jit, we don't provide functionality to wrap jax.jit again.

4.2.1 Code

from brax import envs
import brax.jumpy as jp

from gnwrapper.brax import GymHTML

rng = jp.random_prngkey(seed=42)
rng, rng_use = jp.random_split(rng)

ant = GymHTML(envs.create_gym_env("ant", auto_reset=False, seed=0), video_callable = lambda ep: True)

for i in range(2):
    obs = ant.reset()
    while True:
        rng, rng_use = jp.random_split(rng)
        obs, rew, done, _ = ant.step(jp.random_uniform(rng_use, ant.action_space.shape))
        if done:
		    # When `done = True`, the episode is written at html file.
            break

# We can get list of recorded episodes.
episodes = ant.recorded_episodes()

# `display()` method show all recorded episodes.
# `display(1)` shows only episode 1, if it is recorded
# `display([1, 2])` shows episode 1 & 2, if they are recorded, etc.
ant.display()

4.2.2 Parameters

Argument	Type	Description
env	brax.envs.Env	Environment
directory=None	Optional[str]	Directory to store html. If None(default), time stamp ("%Y%m%d-%H%M%S") is used.
heght=480	int	Viewer height in px. (There is a Brax bug (this issue), however, PR was merged.)
video_callable=None	Optional[Callable[[int], bool]]	Function to determine whether each episode is recorded or not. If None (default), every 1000 and cubic number less than 1000 are recorded

4.3 Limitation

Since done is always False, auto reset (aka. brax.envs.wrappers.AutoResetWrapper) is not supported. You must call brax.envs.create() or brax.envs.create_gym_env() with auto_reset=False argument.

Vectorized (batched) environments (aka. brax.envs.wrappers.VectorWrapper, brax.envs.wrappers.GymVectorWrapper) are not supported, too. You should not specify batch_size argument at brax.envs.create() or brax.envs.create_gym_env().

5. Links

• Repository at GitHub