Unity ML-Agents Toolkit
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.
- 15+ 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 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 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 2. Click
to get started with the latest release of ML-Agents.
The table below lists all our releases, including our
master 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.
|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 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 published a series of blog posts that are relevant for ML-Agents:
- (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)
In addition to our own documentation, here are some additional, relevant articles:
- A Game Developer Learns Machine Learning
- Explore Unity Technologies ML-Agents Exclusively on Intel Architecture
- ML-Agents Penguins tutorial
Community and Feedback
For problems with the installation and setup of the 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 requests, 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 firstname.lastname@example.org.