[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9591":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":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":15,"lastSyncTime":37,"discoverSource":38},9591,"ml-agents","Unity-Technologies\u002Fml-agents","Unity-Technologies","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.","https:\u002F\u002Funity.com\u002Fproducts\u002Fmachine-learning-agents",null,"C#",19482,4458,555,2,0,1,21,90,11,45,"Other",false,"develop",true,[27,28,29,30,31,32,33],"deep-learning","deep-reinforcement-learning","machine-learning","neural-networks","reinforcement-learning","unity","unity3d","2026-06-12 02:02:09","# Unity ML-Agents Toolkit\n\n[![docs badge](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdocs-reference-blue.svg)](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest)\n\n[![license badge](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache--2.0-green.svg)](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Fblob\u002Frelease\u002F4.0.0\u002FLICENSE.md)\n\n([latest release](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Freleases\u002Ftag\u002Flatest_release)) ([all releases](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Freleases))\n\n**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. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR\u002FAR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities.\n\n## Features\n- 17+ [example Unity environments](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FLearning-Environment-Examples.html)\n- Support for multiple environment configurations and training scenarios\n- Flexible Unity SDK that can be integrated into your game or custom Unity scene\n- Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).\n- Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).\n- Quickly and easily add your own [custom training algorithm](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FPython-Custom-Trainer-Plugin.html) and\u002For components.\n- Easily definable Curriculum Learning scenarios for complex tasks\n- Train robust agents using environment randomization\n- Flexible agent control with On Demand Decision Making\n- Train using multiple concurrent Unity environment instances\n- Utilizes the [Inference Engine](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FInference-Engine.html) to provide native cross-platform support\n- Unity environment [control from Python](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FPython-LLAPI.html)\n- Wrap Unity learning environments as a [gym](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FPython-Gym-API.html) environment\n- Wrap Unity learning environments as a [PettingZoo](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FPython-PettingZoo-API.html) environment\n\n## Releases & Documentation\n\n> **⚠️ Documentation Migration Notice**\n> We have moved to [Unity Package documentation](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest) as the **primary developer documentation** and have **deprecated** the maintenance of [web docs](https:\u002F\u002Funity-technologies.github.io\u002Fml-agents\u002F). Please use the Unity Package documentation for the most up-to-date information.\n\nThe table below shows our latest release, including our `develop` branch which is under active development and may be unstable. A few helpful guidelines:\n\n- The [Versioning page](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FVersioning.html) overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components.\n- The [Releases page](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Freleases) contains details of the changes between releases.\n- The [Migration page](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FMigrating.html) contains details on how to upgrade from earlier releases of the ML-Agents Toolkit.\n- The `com.unity.ml-agents` package is [verified](https:\u002F\u002Fdocs.unity3d.com\u002F2020.1\u002FDocumentation\u002FManual\u002Fpack-safe.html) for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x.\n\n|      **Version**       |  **Release Date**   |                                  **Source**                                   |                                                 **Documentation**                                                  |                                      **Download**                                      |                  **Python Package**                   |                                   **Unity Package**                                   |\n|:----------------------:|:-------------------:|:-----------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------:|:-----------------------------------------------------:|:-------------------------------------------------------------------------------------:|\n|     **Release 23**     | **August 28, 2025** | **[source](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Ftree\u002Frelease_23)** |              **[docs](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@4.0\u002Fmanual\u002Findex.html)**               | **[download](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Farchive\u002Frelease_23.zip)** | **[1.1.0](https:\u002F\u002Fpypi.org\u002Fproject\u002Fmlagents\u002F1.1.0\u002F)** |                                       **4.0.0**                                       |\n| **develop (unstable)** |         --          |    [source](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Ftree\u002Fdevelop)     | [docs](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Ftree\u002Fdevelop\u002Fcom.unity.ml-agents\u002FDocumentation~\u002Findex.md)   |    [download](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Farchive\u002Fdevelop.zip)     |                         --                            |                                          --                                           |\n\n\n\nIf you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our [reference paper on Unity and the ML-Agents Toolkit](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02627).\n\nIf you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference:\n\n```\n@article{juliani2020,\n  title={Unity: A general platform for intelligent agents},\n  author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny},\n  journal={arXiv preprint arXiv:1809.02627},\n  url={https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.02627.pdf},\n  year={2020}\n}\n```\n\nAdditionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference:\n\n```\n@article{cohen2022,\n  title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning},\n  author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy},\n  journal={RL in Games Workshop AAAI 2022},\n  url={http:\u002F\u002Faaai-rlg.mlanctot.info\u002Fpapers\u002FAAAI22-RLG_paper_32.pdf},\n  year={2022}\n}\n```\n\n\n## Additional Resources\n\n* [Unity Discussions](https:\u002F\u002Fdiscussions.unity.com\u002Ftag\u002Fml-agents)\n* [ML-Agents tutorials by CodeMonkeyUnity](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110)\n* [Introduction to ML-Agents by Huggingface](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fdeep-rl-course\u002Fen\u002Funit5\u002Fintroduction)\n* [Community created ML-Agents projects](https:\u002F\u002Fdiscussions.unity.com\u002Ft\u002Fpost-your-ml-agents-project\u002F816756)\n* [ML-Agents models on Huggingface](https:\u002F\u002Fhuggingface.co\u002Fmodels?library=ml-agents)\n* [Blog posts](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FBlog-posts.html)\n* [Discord](https:\u002F\u002Fdiscord.com\u002Fchannels\u002F489222168727519232\u002F1202574086115557446)\n\n## Community and Feedback\n\nThe ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our [contribution guidelines](https:\u002F\u002Fdocs.unity3d.com\u002FPackages\u002Fcom.unity.ml-agents@latest\u002Findex.html?subfolder=\u002Fmanual\u002FCONTRIBUTING.html) and [code of conduct](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Fblob\u002Frelease\u002F4.0.0\u002FCODE_OF_CONDUCT.md).\n\nFor problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the [Unity ML-Agents discussion forum](https:\u002F\u002Fdiscussions.unity.com\u002Ftag\u002Fml-agents). Be sure to include as many details as possible to help others assist you effectively. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please [submit a GitHub issue](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents\u002Fissues).\n\nPlease tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to [this discussion thread](https:\u002F\u002Fdiscussions.unity.com\u002Ft\u002Fhelp-shape-the-future-of-ml-agents\u002F1661019).\n\n## Privacy\n\nIn order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the [Unity Privacy Policy](https:\u002F\u002Funity3d.com\u002Flegal\u002Fprivacy-policy).\n","Unity ML-Agents Toolkit 是一个开源项目，旨在让游戏和模拟环境成为训练智能代理的平台。它基于PyTorch实现了多种先进的算法，支持通过深度强化学习、模仿学习等技术训练2D、3D及VR\u002FAR游戏中的智能体。该工具包提供了一个易于使用的Python API，便于开发者和研究人员在不同场景下训练代理，如控制非玩家角色的行为、自动化测试以及预发布阶段的游戏设计评估。其核心功能包括支持单个或多个代理协同\u002F竞争训练、从演示中学习的能力、自定义训练算法的灵活性以及跨平台支持等。适用于希望结合人工智能提升游戏体验的游戏开发者，以及需要复杂交互环境进行研究的人工智能研究者。","2026-06-11 03:23:37","top_topic"]