[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-10316":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},10316,"PettingZoo","Farama-Foundation\u002FPettingZoo","Farama-Foundation","An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities","https:\u002F\u002Fpettingzoo.farama.org",null,"Python",3439,493,16,28,0,1,8,37,5,30.08,"Other",false,"master",true,[27,28,29,30,31,32],"api","gym","gymnasium","multi-agent-reinforcement-learning","multiagent-reinforcement-learning","reinforcement-learning","2026-06-12 02:02:20","[![pre-commit](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https:\u002F\u002Fpre-commit.com\u002F) [![Code style: black](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcode%20style-black-000000.svg)](https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack)\n\n\u003Cp align=\"center\">\n    \u003Ca href = \"https:\u002F\u002Fpettingzoo.farama.org\u002F\" target = \"_blank\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002FFarama-Foundation\u002FPettingZoo\u002Fmaster\u002Fpettingzoo-text.png\" width=\"500px\"\u002F> \u003C\u002Fa>\n\u003C\u002Fp>\n\nPettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of [Gymnasium](https:\u002F\u002Fgithub.com\u002FFarama-Foundation\u002FGymnasium).\n\nThe documentation website is at [pettingzoo.farama.org](https:\u002F\u002Fpettingzoo.farama.org) and we have a public discord server (which we also use to coordinate development work) that you can join here: https:\u002F\u002Fdiscord.gg\u002FnhvKkYa6qX\n\n## Environments\n\nPettingZoo includes the following families of environments:\n\n* [Atari](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fatari\u002F): Multi-player Atari 2600 games (cooperative, competitive and mixed sum)\n* [Butterfly](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fbutterfly): Cooperative graphical games developed by us, requiring a high degree of coordination\n* [Classic](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fclassic): Classical games including card games, board games, etc.\n* [SISL](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fsisl): 2 cooperative environments, originally from https:\u002F\u002Fgithub.com\u002Fsisl\u002FMADRL\n\n## Installation\n\nTo install the base PettingZoo library: `pip install pettingzoo`.\n\nThis does not include dependencies for all families of environments (some environments can be problematic to install on certain systems).\n\nTo install the dependencies for one family, use `pip install 'pettingzoo[atari]'`, or use `pip install 'pettingzoo[all]'` to install all dependencies.\n\nWe support and maintain PettingZoo for Python 3.9, 3.10, 3.11, 3.12, 3.13, and 3.14 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.\n\nNote: Some Linux distributions may require manual installation of `cmake`, `swig`, or `zlib1g-dev` (e.g., `sudo apt install cmake swig zlib1g-dev`)\n\n## Getting started\n\nFor an introduction to PettingZoo, see [Basic Usage](https:\u002F\u002Fpettingzoo.farama.org\u002Fcontent\u002Fbasic_usage\u002F). To create a new environment, see our [Environment Creation Tutorial](https:\u002F\u002Fpettingzoo.farama.org\u002Ftutorials\u002Fcustom_environment\u002F1-project-structure\u002F) and [Custom Environment Examples](https:\u002F\u002Fpettingzoo.farama.org\u002Fcontent\u002Fenvironment_creation\u002F).\nFor examples of training RL models using PettingZoo see our tutorials:\n* [CleanRL: Implementing PPO](https:\u002F\u002Fpettingzoo.farama.org\u002Ftutorials\u002Fcleanrl\u002Fimplementing_PPO\u002F): train multiple PPO agents in the [Pistonball](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fbutterfly\u002Fpistonball\u002F) environment.\n* [Tianshou: Training Agents](https:\u002F\u002Fpettingzoo.farama.org\u002Ftutorials\u002Ftianshou\u002Fintermediate\u002F): train DQN agents in the [Tic-Tac-Toe](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fclassic\u002Ftictactoe\u002F) environment.\n* [AgileRL: Training, Curriculums and Self-play](https:\u002F\u002Fpettingzoo.farama.org\u002Fmain\u002Ftutorials\u002Fagilerl\u002FDQN\u002F): train agents with curriculum learning and self-play in the [Connect Four](https:\u002F\u002Fpettingzoo.farama.org\u002Fenvironments\u002Fclassic\u002Fconnect_four\u002F) environment.\n\n## API\n\nPettingZoo model environments as [*Agent Environment Cycle* (AEC) games](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.14471.pdf), in order to be able to cleanly support all types of multi-agent RL environments under one API and to minimize the potential for certain classes of common bugs.\n\nUsing environments in PettingZoo is very similar to Gymnasium, i.e. you initialize an environment via:\n\n```python\nfrom pettingzoo.butterfly import pistonball_v6\nenv = pistonball_v6.env()\n```\n\nEnvironments can be interacted with in a manner very similar to Gymnasium:\n\n```python\nenv.reset()\nfor agent in env.agent_iter():\n    observation, reward, termination, truncation, info = env.last()\n    action = None if termination or truncation else env.action_space(agent).sample()  # this is where you would insert your policy\n    env.step(action)\n```\n\nFor the complete API documentation, please see https:\u002F\u002Fpettingzoo.farama.org\u002Fapi\u002Faec\u002F\n\n### Parallel API\n\nIn certain environments, it's a valid to assume that agents take their actions at the same time. For these games, we offer a secondary API to allow for parallel actions, documented at https:\u002F\u002Fpettingzoo.farama.org\u002Fapi\u002Fparallel\u002F\n\n## SuperSuit\n\nSuperSuit is a library that includes all commonly used wrappers in RL (frame stacking, observation, normalization, etc.) for PettingZoo and Gymnasium environments with a nice API. We developed it in lieu of wrappers built into PettingZoo. https:\u002F\u002Fgithub.com\u002FFarama-Foundation\u002FSuperSuit\n\n## Environment Versioning\n\nPettingZoo keeps strict versioning for reproducibility reasons. All environments end in a suffix like \"\\_v0\".  When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.\n\n## Citation\n\nTo cite this project in publication, please use\n\n```\n@article{terry2021pettingzoo,\n  title={Pettingzoo: Gym for multi-agent reinforcement learning},\n  author={Terry, J and Black, Benjamin and Grammel, Nathaniel and Jayakumar, Mario and Hari, Ananth and Sullivan, Ryan and Santos, Luis S and Dieffendahl, Clemens and Horsch, Caroline and Perez-Vicente, Rodrigo and others},\n  journal={Advances in Neural Information Processing Systems},\n  volume={34},\n  pages={15032--15043},\n  year={2021}\n}\n```\n\n## Project Maintainers\n- Project Manager: [Travis Virgil](https:\u002F\u002Fgithub.com\u002Fvirgilt) - `travis@farama.org`\n- Maintainer: [Albert Han](https:\u002F\u002Fgithub.com\u002Fyjhan96) - `yjhan96@gmail.com`.\n- Maintenance for this project is also contributed by the broader Farama team: [farama.org\u002Fteam](https:\u002F\u002Ffarama.org\u002Fteam).\n","PettingZoo 是一个用于多智能体强化学习研究的Python库，类似于Gymnasium的多智能体版本。它提供了一个统一的API标准，支持多种环境类型，包括Atari游戏、Butterfly系列（需要高度协调的图形化合作游戏）、经典棋盘和纸牌游戏以及SISL中的两个合作环境。PettingZoo采用Python编写，并通过预提交钩子和Black代码风格确保了代码质量的一致性。此项目非常适合于需要构建或测试多智能体系统的研究人员及开发者使用，在学术研究与教育领域尤其有用。",2,"2026-06-11 03:27:43","top_topic"]