The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. 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.
- Unity environment control from Python
- 10+ sample Unity environments
- Support for multiple environment configurations and training scenarios
- Train memory-enhanced agents using deep reinforcement learning
- Easily definable Curriculum Learning scenarios
- Broadcasting of agent behavior for supervised learning
- Built-in support for Imitation Learning
- Flexible agent control with On Demand Decision Making
- Visualizing network outputs within the environment
- Simplified set-up with Docker
- Wrap learning environments as a gym
- For more information, in addition to installation and usage instructions, see our documentation home.
- 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. Also, see below for instructions on citing this paper.
- If you have used a version of the ML-Agents toolkit prior to v0.5, we strongly recommend our guide on migrating from earlier versions.
We have published a series of blog posts that are relevant for ML-Agents:
- Overviewing reinforcement learning concepts (multi-armed bandit and Q-learning)
- Using Machine Learning Agents in a real game: a beginner’s guide
- Post announcing the winners of our first ML-Agents Challenge
- Post overviewing how Unity can be leveraged as a simulator to design safer cities.
In addition to our own documentation, here are some additional, relevant articles:
- Unity AI - Unity 3D Artificial Intelligence
- A Game Developer Learns Machine Learning
- Explore Unity Technologies ML-Agents Exclusively on Intel Architecture
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.
You can connect with us and the broader community through Unity Connect and GitHub:
- Join our Unity Machine Learning Channel to connect with others using the ML-Agents toolkit and Unity developers enthusiastic about machine learning. We use that channel to surface updates regarding the ML-Agents toolkit (and, more broadly, machine learning in games).
- If you run into any problems using the ML-Agents toolkit, submit an issue and make sure to include as much detail as possible.
- 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 [email protected].
To make the Unity ML-Agents toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community.
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., 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.