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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
• 15+ sample Unity environments
• Two deep reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC)
• Support for multiple environment configurations and training scenarios
• Self-play mechanism for training agents in adversarial scenarios
• Train memory-enhanced agents using deep reinforcement learning
• Easily definable Curriculum Learning and Generalization scenarios
• Built-in support for Imitation Learning through Behavioral Cloning or Generative Adversarial Imitation Learning
• Flexible agent control with On Demand Decision Making
• Visualizing network outputs within the environment
• Wrap learning environments as a gym
• Utilizes the Unity Inference Engine
• Train using concurrent Unity environment instances

Our latest, stable release is 0.15.0. Click here 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 docs 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.
• See the GitHub releases for more details of the changes between versions.
• If you have used an earlier version of the ML-Agents Toolkit, we strongly recommend our guide on migrating from earlier versions.

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., 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.

We have published a series of blog posts that are relevant for ML-Agents:

• (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

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 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 [email protected].

Apache License 2.0