Arcade-Learning-Environment
Shimmy
| Arcade-Learning-Environment | Shimmy | |
|---|---|---|
| 6 | 1 | |
| 2,080 | 118 | |
| 0.7% | 3.4% | |
| 5.3 | 6.2 | |
| 5 days ago | about 2 months ago | |
| C++ | Python | |
| GNU General Public License v3.0 only | MIT License |
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Arcade-Learning-Environment
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
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How to apply Deep RL in Arcade Learning Environment?
They are talking about this: https://github.com/mgbellemare/Arcade-Learning-Environment
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How are rewards/scores calculated in openai Gym's Atari Skiing-v0?
The code for Atari envs is not on gym, but on ALE
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Merge Dragon Bot
If you're more interested in playing games directly from pixel-level input, check out the Arcade Learning Environment, which lets you do this with all the old Atari games. You can find lots of tutorials online about using "reinforcement learning" to play these games.
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[News] The Arcade Learning Environment: Version 0.7
I glanced over everything in this post, for a more detailed explainer check out the following blog post: https://brosa.ca/blog/ale-release-v0.7 and the release notes at https://github.com/mgbellemare/Arcade-Learning-Environment/releases/tag/v0.7.0.
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ROM differences in Atari gym
I'm running some experiments on Atari via gym and have noticed that the MD5 checksums on around half of the ROMs supplied by gym[atari] differ from the MD5s listed here. Has anyone noticed this before, and would it make a difference to the results?
Shimmy
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
Check out our blog post or release notes for more details. Our documentation website at https://shimmy.farama.org/, containing an overview of each environment with screenshots, installation instructions, full usage scripts, and API information.
What are some alternatives?
lab - A customisable 3D platform for agent-based AI research
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
bsuite - bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
meltingpot - A suite of test scenarios for multi-agent reinforcement learning.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.