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.
See our ML-Agents Overview page for detailed descriptions of all these features.
Our latest, stable release is
Release 15. Click
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:
com.unity.ml-agentspackage 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|
|Release 15||March 17, 2021||source||docs||download||0.25.0||1.9.0|
|Verified Package 1.0.7||March 8, 2021||source||docs||download||0.16.1||1.0.7|
|Release 14||March 5, 2021||source||docs||download||0.24.1||1.8.1|
|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|
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.
We have a Unity Learn course, ML-Agents: Hummingsbird, that provides a gentle introduction to Unity and the ML-Agents Toolkit.
We have also published a series of blog posts that are relevant for ML-Agents:
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.
Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread.
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].