arcade-learning-environment 0.11.0

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Created by brew bump

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release notes

This release adds (an experiment) built-in vectorisation environment, available through `gymnasium.make_vec("ALE/{game_name}-v5", num_envs)` or `ale_py.AtariVectorEnv("{rom_name}", num_envs)`.
import gymnasium as gym
import ale_py

gym.register_envs(ale_py)

envs = gym.make_vec("ALE/Pong-v5")
observations, infos = envs.reset()

for i in range(100):
  actions = envs.action_space.sample()
  observations, rewards, terminations, truncations, infos = envs.step(actions)

envs.close()
Vectorisation is a crucial feature of RL to help increase the sample rate of environments through sampling multiple sub-environments at the same time.

Gymnasium provides a generalised vectorisation capability, however, is relatively slow due its python implementation.

For faster implementations, EnvPool provide C++ vectorisation that significantly increase the sample speed but it no longer maintained.

Inspired by the EnvPool implementation, we've implemented an asynchronous vectorisation environment in C++, in particular, the standard Atari preprocessing including frame skipping, frame stacking, observation resizing, etc.
For full documentation of the vector environment, see this page.
We will continue building out this vectorisation to include XLA support, improved preprocessing and auto resetting.
As this is an experimental feature, we wish to hear about any bugs, problems or features to add. Raise an issue on GitHub or ask a question on the Farama Discord server.

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