Unity Machine Learning Agents Toolkit
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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. 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.
See our ML-Agents Overview page for detailed descriptions of all these features.
**Our latest, stable release is
. Clickhereto get started with the latest release of ML-Agents.**
The table below lists all our releases, including our
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.
| Version | Release Date | Source | Documentation | Download | |:-------:|:------:|:-------------:|:-------:|:------------:| | master (unstable) | -- | source | docs | download | | Release 3 | June 10, 2020 | source | docs | download | | Release 2 | May 20, 2020 | source | docs | download | | Release 1 | April 30, 2020 | source | docs | download | | 0.15.1 | March 30, 2020 | source | docs | download | | 0.15.0 | March 18, 2020 | source | docs | download | | 0.14.1 | February 26, 2020 | source | docs | download | | 0.14.0 | February 13, 2020 | source | docs | download | | 0.13.1 | January 21, 2020 | source | docs | download | | 0.13.0 | January 8, 2020 | source | docs | download |
If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of ourreference 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 preprintarXiv:1809.02627._https://github.com/Unity-Technologies/ml-agents.
We have published a series of blog posts that are relevant for ML-Agents:
In addition to our own documentation, here are some additional, relevant articles:
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 theUnity 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, pleasesubmit 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 tolet us know about it.
For any other questions or feedback, connect directly with the ML-Agents team at [email protected].