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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation
| Date | Stars |
|---|---|
| 2026-07-31 | 366 |
| 2026-08-06 | 366 |
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<img src="https://raw.githubusercontent.com/diambra/arena/main/img/github.jpg" alt="diambra" width="100%"/> <p align="center"> <a href="https://docs.diambra.ai">Documentation</a> • <a href="https://diambra.ai/">Website</a> </p> <p align="center"> <a href="https://www.linkedin.com/company/diambra">Linkedin</a> • <a href="https://diambra.ai/discord">Discord</a> • <a href="https://www.twitch.tv/diambra_ai">Twitch</a> • <a href="https://www.youtube.com/c/diambra_ai">YouTube</a> • <a href="https://twitter.com/diambra_ai">Twitter</a> </p> <p align="center"> <a href="https://arxiv.org/abs/2210.10595"><img src="https://img.shields.io/badge/paper-arXiv:2210.10595-B31B1B?logo=arxiv" alt="Paper"/></a> </p> <p align="center"> <a href="https://github.com/diambra/arena/actions/workflows/test.yaml"><img src="https://img.shields.io/github/actions/workflow/status/diambra/arena/test.yaml?label=arena%20tests&logo=github" alt="Arena Test"/></a> <a href="https://github.com/diambra/arena/actions/workflows/test_agents.yaml"><img src="https://img.shields.io/github/actions/workflow/status/diambra/arena/test_agents.yaml?label=agents%20tests&logo=github" alt="Agents Test"/></a> <a href="https://github.com/diambra/arena/tags"><img src="https://img.shields.io/github/v/tag/diambra/arena?label=latest%20tag&logo=github" alt="Latest Tag"/></a> <a href="https://pypi.org/project/diambra-arena/"><img src="https://img.shields.io/pypi/v/diambra-arena?logo=pypi" alt="Pypi version"/></a> </p> <p align="center"> <a href="https://docs.diambra.ai/#installation"><img src="https://img.shields.io/badge/supported%20os-linux%20%7C%20win%20%7C%20macOS-blue?logo=docker" alt="Supported OS"/></a> <a href="https://docs.diambra.ai/"><img src="https://img.shields.io/github/last-commit/diambra/docs/main?label=docs%20last%20update&logo=readthedocs" alt="Last Docs Update"/></a> </p> # DIAMBRA Arena ## Index - **[Overview](#overview)** - **[Competition Platform](#competition-platform)** - **[Installation](#installation)** - **[Quickstart & Examples](#quickstart--examples)** - **[Reinforcement Learning Libs Compatibility](#reinforcement-learning-libs-compatibility)** - **[References](#references)** - **[Support, Feature Requests & Bugs Reports](#support-feature-requests--bugs-reports)** - **[Citation](#citation)** - **[Terms of Use](#terms-of-use)** ## Overview DIAMBRA Arena is a software package featuring a collection of **high-quality environments for Reinforcement Learning research and experimentation**. It provides a standard interface to popular arcade emulated video games, offering a **Python API fully compliant with OpenAI Gym/Gymnasium format**, that makes its adoption smooth and straightforward. It **supports all major Operating Systems** (Linux, Windows and MacOS) and **can be easily installed via Python PIP**, as described in the **[installation section](#installation)** below. It is **completely free to use**, the user only needs to <a href="https://diambra.ai/register/" target="_blank">register on the official website</a>. In addition, it comes with a <a href="https://docs.diambra.ai" target="_blank">comprehensive documentation</a>, and this repository provides a **collection of examples** covering main use cases of interest **that can be run in just a few steps**. #### Main Features All environments are episodic Reinforcement Learning tasks, with discrete actions (gamepad buttons) and observations composed by screen pixels plus specific RAM states (like characters health bars or characters stage side). They all **support both single player (1P) as well as two players (2P) mode**, making them the perfect resource to explore all the following Reinforcement Learning subfields: | <img src="https://raw.githubusercontent.com/diambra/arena/main/img/AIvsCOM.png" alt="standardRl" width="125"/> | <img src="https://raw.githubusercontent.com/diambra/arena/main/img/AIvsAI.png" alt="competitiveMa" width="125"/> | <img src="https://raw.githubusercontent.com/
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2f4e9644298a2e0d, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning