# Unity ML-Agents Toolkit [](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/) [](LICENSE) ([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release)) ([all releases](https://github.com/Unity-Technologies/ml-agents/releases)) **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/AR 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. ## Features - 15+ [example Unity environments](docs/Learning-Environment-Examples.md) - Support for multiple environment configurations and training scenarios - Flexible Unity SDK that can be integrated into your game or custom Unity scene - Training using two deep reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) - Built-in support for Imitation Learning through Behavioral Cloning or Generative Adversarial Imitation Learning - Self-play mechanism for training agents in adversarial scenarios - Easily definable Curriculum Learning scenarios for complex tasks - Train robust agents using environment randomization - Flexible agent control with On Demand Decision Making - Train using multiple concurrent Unity environment instances - Utilizes the [Unity Inference Engine](docs/Unity-Inference-Engine.md) to provide native cross-platform support - Unity environment [control from Python](docs/Python-API.md) - Wrap Unity learning environments as a [gym](gym-unity/README.md) See our [ML-Agents Overview](docs/ML-Agents-Overview.md) page for detailed descriptions of all these features. ## Releases & Documentation **Our latest, stable release is `Release 14`. Click [here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md) to get started with the latest release of ML-Agents.** The table below lists all our releases, including our `main` branch which is under active development and may be unstable. A few helpful guidelines: - The [Versioning page](docs/Versioning.md) overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components. - The [Releases page](https://github.com/Unity-Technologies/ml-agents/releases) contains details of the changes between releases. - The [Migration page](docs/Migrating.md) contains details on how to upgrade from earlier releases of the ML-Agents Toolkit. - The **Documentation** links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using. - The `com.unity.ml-agents` package is [verified](https://docs.unity3d.com/2020.1/Documentation/Manual/pack-safe.html) for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x. | **Version** | **Release Date** | **Source** | **Documentation** | **Download** | **Python Package** | **Unity Package** | |:-------:|:------:|:-------------:|:-------:|:------------:|:------------:|:------------:| | **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) | -- | -- | | **Release 14** | **March 5, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_14)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_14.zip)** | **[0.24.1](https://pypi.org/project/mlagents/0.24.1/)** | **[1.8.1](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.8/manual/index.html)** | | **Release 13** | February 17, 2021 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_13) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_13.zip) | [0.24.0](https://pypi.org/project/mlagents/0.24.0/) | [1.8.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.8/manual/index.html) | | **Release 12** | December 22, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_12) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_12.zip) | [0.23.0](https://pypi.org/project/mlagents/0.23.0/) | [1.7.2](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.7/manual/index.html) | | **Release 11** | December 21, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_11) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_11_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_11.zip) | [0.23.0](https://pypi.org/project/mlagents/0.23.0/) | [1.7.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.7/manual/index.html) | | **Release 10** | November 18, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_10) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_10_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_10.zip) | [0.22.0](https://pypi.org/project/mlagents/0.22.0/) | [1.6.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.6/manual/index.html) | | **Verified Package 1.0.6** | **November 16, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.6)** | **[docs](https://github.com/Unity-Technologies/ml-agents/blob/release_2_verified_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.6.zip)** | **[0.16.1](https://pypi.org/project/mlagents/0.16.1/)** | **[1.0.6](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html)** | | **Release 9** | November 4, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_9) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_9.zip) | [0.21.1](https://pypi.org/project/mlagents/0.21.1/) | [1.5.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.5/manual/index.html) | | **Release 8** | October 14, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_8) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_8.zip) | [0.21.0](https://pypi.org/project/mlagents/0.21.0/) | [1.5.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.5/manual/index.html) | 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). If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference: Juliani, A., Berges, V., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., Lange, D. (2020). Unity: A General Platform for Intelligent Agents. _arXiv preprint [arXiv:1809.02627](https://arxiv.org/abs/1809.02627)._ https://github.com/Unity-Technologies/ml-agents. ## Additional Resources We have a Unity Learn course, [ML-Agents: Hummingsbird](https://learn.unity.com/course/ml-agents-hummingbirds), that provides a gentle introduction to Unity and the ML-Agents Toolkit. We've also partnered with [CodeMonkeyUnity](https://www.youtube.com/c/CodeMonkeyUnity) to create a [series of tutorial videos](https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110) on how to implement and use the ML-Agents Toolkit. We have also published a series of blog posts that are relevant for ML-Agents: - (December 28, 2020) [Happy holidays from the Unity ML-Agents team!](https://blogs.unity3d.com/2020/12/28/happy-holidays-from-the-unity-ml-agents-team/) - (November 20, 2020) [How Eidos-Montréal created Grid Sensors to improve observations for training agents](https://blogs.unity3d.com/2020/11/20/how-eidos-montreal-created-grid-sensors-to-improve-observations-for-training-agents/) - (November 11, 2020) [2020 AI@Unity interns shoutout](https://blogs.unity3d.com/2020/11/11/2020-aiunity-interns-shoutout/) - (May 12, 2020) [Announcing ML-Agents Unity Package v1.0!](https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/) - (February 28, 2020) [Training intelligent adversaries using self-play with ML-Agents](https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/) - (November 11, 2019) [Training your agents 7 times faster with ML-Agents](https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/) - (October 21, 2019) [The AI@Unity interns help shape the world](https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/) - (April 15, 2019) [Unity ML-Agents Toolkit v0.8: Faster training on real games](https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/) - (March 1, 2019) [Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference](https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/) - (December 17, 2018) [ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning](https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/) - (October 2, 2018) [Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit](https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/) - (September 11, 2018) [ML-Agents Toolkit v0.5, new resources for AI researchers available now](https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/) - (June 26, 2018) [Solving sparse-reward tasks with Curiosity](https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/) - (June 19, 2018) [Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree](https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/) - (May 24, 2018) [Imitation Learning in Unity: The Workflow](https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/) - (March 15, 2018) [ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more](https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/) - (December 11, 2017) [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/) - (December 8, 2017) [Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more](https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/) - (September 19, 2017) [Introducing: Unity Machine Learning Agents Toolkit](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/) - Overviewing reinforcement learning concepts ([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/) and [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/)) ## Community and Feedback The 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](com.unity.ml-agents/CONTRIBUTING.md) and [code of conduct](CODE_OF_CONDUCT.md). For 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 forum](https://forum.unity.com/forums/ml-agents.453/) and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please [submit a GitHub issue](https://github.com/Unity-Technologies/ml-agents/issues). Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to [let us know about it](https://github.com/Unity-Technologies/ml-agents/issues/1454). For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com. ## Privacy In 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://unity3d.com/legal/privacy-policy). ## License [Apache License 2.0](LICENSE)