Arcade-Learning-Environment
gym
| Arcade-Learning-Environment | gym | |
|---|---|---|
| 6 | 96 | |
| 2,080 | 33,933 | |
| 0.7% | 0.5% | |
| 5.3 | 0.0 | |
| 6 days ago | 13 days 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?
gym
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OpenAI Acquires Global Illumination
A co-founder announced they disbanded their robots team a couple years ago: https://venturebeat.com/business/openai-disbands-its-robotic...
That was the same time they depreciated OpenAI Gym: https://github.com/openai/gym
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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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Some confusion about variables and functions in mujoco-py
When I browse fetch_env.py, I have a question about the following code snippet:
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pip install stable-baselines3[extra]
Nvm, this works for me '!pip install setuptools==65.5.0' Source: https://github.com/openai/gym/issues/3176
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
how would this interact/compare with https://github.com/openai/gym?
- What has replaced OpenAI Retro Gym?
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Understanding Reinforcement Learning
If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.
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Using the cross-entropy method to solve Frozen Lake
Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.
- Is there a publicly available state space model for the Lunar Lander environment?
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How to Create a Behavioral Cloning Bot to Play Online Games?
typically a more relaxed approach is taken via reinforcement learning, but it requires that you can simulate the game via a given gamestate. take a look at e.g. https://www.gymlibrary.dev/
What are some alternatives?
Shimmy - An API conversion tool for popular external reinforcement learning environments
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
lab - A customisable 3D platform for agent-based AI research
carla - Open-source simulator for autonomous driving research.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
tensorflow - An Open Source Machine Learning Framework for Everyone
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.
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.