Unity ML-Agents Toolkit

(latest release) (all 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

• 18+ example Unity environments
• 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 (BC) or Generative Adversarial Imitation Learning (GAIL)
• 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 to provide native cross-platform support
• Unity environment control from Python
• Wrap Unity learning environments as a gym

See our ML-Agents Overview page for detailed descriptions of all these features.

Releases & Documentation

Our latest, stable release is Release 13. Click here 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 overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components.
• The Releases page contains details of the changes between releases.
• The Migration page 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 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	docs	download	--	--
Release 13	February 17, 2021	source	docs	download	0.24.0	1.8.0
Release 12	December 22, 2020	source	docs	download	0.23.0	1.7.2
Release 11	December 21, 2020	source	docs	download	0.23.0	1.7.0
Release 10	November 18, 2020	source	docs	download	0.22.0	1.6.0
Verified Package 1.0.6	November 16, 2020	source	docs	download	0.16.1	1.0.6
Release 9	November 4, 2020	source	docs	download	0.21.1	1.5.0
Release 8	October 14, 2020	source	docs	download	0.21.0	1.5.0
Verified Package 1.0.5	September 23, 2020	source	docs	download	0.16.1	1.0.5

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.

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://github.com/Unity-Technologies/ml-agents.

Additional Resources

We have a Unity Learn course, ML-Agents: Hummingsbird, that provides a gentle introduction to Unity and the ML-Agents Toolkit.

We've also partnered with CodeMonkeyUnity to create a series of tutorial videos 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!
• (November 20, 2020) How Eidos-Montréal created Grid Sensors to improve observations for training agents
• (November 11, 2020) 2020 AI@Unity interns shoutout
• (May 12, 2020) Announcing ML-Agents Unity Package v1.0!
• (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents
• (November 11, 2019) Training your agents 7 times faster with ML-Agents
• (October 21, 2019) The AI@Unity interns help shape the world
• (April 15, 2019) 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
• (December 17, 2018) 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
• (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now
• (June 26, 2018) Solving sparse-reward tasks with Curiosity
• (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree
• (May 24, 2018) Imitation Learning in Unity: The Workflow
• (March 15, 2018) ML-Agents Toolkit 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
• (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more
• (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit
• Overviewing reinforcement learning concepts (multi-armed bandit and Q-learning)

More from Unity

• Unity Robotics
• Unity Computer Vision
• Unity Game Simulation

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 and code of conduct.

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 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.

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.

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.

License

Apache License 2.0