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**The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin
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that enables games and simulations to serve as environments for training
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intelligent agents. Agents can be trained using reinforcement learning,
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imitation learning, neuroevolution, or other machine learning methods through
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a simple-to-use Python API. We also provide implementations (based on
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TensorFlow) of state-of-the-art algorithms to enable game developers
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and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games.
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These trained agents can be used for multiple purposes, including
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controlling NPC behavior (in a variety of settings such as multi-agent and
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adversarial), automated testing of game builds and evaluating different game
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design decisions pre-release. The ML-Agents toolkit is mutually beneficial for both game
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developers and AI researchers as it provides a central platform where advances
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in AI can be evaluated on Unity’s rich environments and then made accessible
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to the wider research and game developer communities.
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**The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source
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Unity plugin that enables games and simulations to serve as environments for
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training intelligent agents. Agents can be trained using reinforcement learning,
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imitation learning, neuroevolution, or other machine learning methods through a
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simple-to-use Python API. We also provide implementations (based on TensorFlow)
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of state-of-the-art algorithms to enable game developers and hobbyists to easily
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train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be
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used for multiple purposes, including controlling NPC behavior (in a variety of
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settings such as multi-agent and adversarial), automated testing of game builds
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and evaluating different game design decisions pre-release. The ML-Agents
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toolkit is mutually beneficial for both game developers and AI researchers as it
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provides a central platform where advances in AI can be evaluated on Unity’s
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rich environments and then made accessible to the wider research and game
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developer communities.
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## Features
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* Unity environment control from Python
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* 10+ sample Unity environments
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* Support for multiple environment configurations and training scenarios
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* Train memory-enhanced Agents using deep reinforcement learning
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* Train memory-enhanced agents using deep reinforcement learning
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* Easily definable Curriculum Learning scenarios
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* Broadcasting of Agent behavior for supervised learning
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* Broadcasting of agent behavior for supervised learning
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* Built-in support for Imitation Learning
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* Flexible Agent control with On Demand Decision Making
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* Flexible agent control with On Demand Decision Making
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* Visualizing network outputs within the environment
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* Simplified set-up with Docker
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* Wrap learning environments as a gym
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## Documentation
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* For more information, in addition to installation and usage
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instructions, see our [documentation home](docs/Readme.md).
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* If you have
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used a version of the ML-Agents toolkit prior to v0.4, we strongly recommend
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our [guide on migrating from earlier versions](docs/Migrating.md).
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* For more information, in addition to installation and usage instructions, see
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our [documentation home](docs/Readme.md).
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* If 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://arxiv.org/abs/1809.02627). Also, see below for instructions on citing this paper.
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* If you have used a version of the ML-Agents toolkit prior to v0.5, we strongly
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recommend our [guide on migrating from earlier versions](docs/Migrating.md).
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## References
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## Additional Resources
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We have published a series of blog posts that are relevant for ML-Agents:
and [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
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-[Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
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-[Post](https://blogs.unity3d.com/2018/02/28/introducing-the-winners-of-the-first-ml-agents-challenge/) announcing the winners of our
*[Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
to connect with others using the ML-Agents toolkit and Unity developers
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enthusiastic about machine learning. We use that channel to surface updates
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regarding the ML-Agents toolkit (and, more broadly, machine learning in
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games).
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* If you run into any problems using the ML-Agents toolkit,
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[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
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make sure to include as much detail as possible.
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For any other questions or feedback, connect directly with the ML-Agents
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team at ml-agents@unity3d.com.
@@ -84,8 +94,14 @@ of the documentation to one language (Chinese), but we hope to continue
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translating more pages and to other languages. Consequently,
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we welcome any enhancements and improvements from the community.
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-[Chinese](docs/localized/zh-CN/)
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*[Chinese](docs/localized/zh-CN/)
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## License
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[Apache License 2.0](LICENSE)
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## Citation
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If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference:
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Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D. (2018). Unity: A General Platform for Intelligent Agents. *arXiv preprint arXiv:1809.02627.*https://github.com/Unity-Technologies/ml-agents.
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