![]() ![]() Solving sparse-reward tasks with Curiosity ML-Agents Toolkit v0.5, new resources for AI researchers available now ![]() Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference Unity ML-Agents Toolkit v0.8: Faster training on real games Training your agents 7 times faster with ML-Agents Training intelligent adversaries using self-play with ML-Agents How Eidos-Montréal created Grid Sensors to improve observations for training agents Happy holidays from the Unity ML-Agents team! ML-Agents v2.0 release: Now supports training complex cooperative behaviors We have also published a series of blog posts that are relevant for ML-Agents: On how to implement and use the ML-Agents Toolkit. That provides a gentle introduction to Unity and the ML-Agents Toolkit. If you use Unity or the ML-Agents Toolkit to conduct research, we ask that youĬite the following paper as a the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning},Īuthor=, Reference paper on Unity and the ML-Agents Toolkit. If you are a researcher interested in a discussion of Unity as an AI platform, Verified packages releases are numbered 1.0.x. The -agents package is verifiedįor Unity 2020.1 and later.Remember to always use theĭocumentation that corresponds to the release version you're using. The Documentation links in the table below include installation and usage.The Migration page contains details on how to upgradeįrom earlier releases of the ML-Agents Toolkit.Releases and the versioning process for each of the ML-Agents components.Ĭontains details of the changes between releases. The Versioning page overviews how we manage our GitHub.Under active development and may be unstable. The table below lists all our releases, including our main branch which is To get started with the latest release of ML-Agents. Our latest, stable release is Release 20. See our ML-Agents Overview page for detailedĭescriptions of all these features. Wrap Unity learning environments as a PettingZoo environment.Wrap Unity learning environments as a gym environment.Train using multiple concurrent Unity environment instances.Flexible agent control with On Demand Decision Making.Train robust agents using environment randomization.Easily definable Curriculum Learning scenarios for complex tasks.Quickly and easily add your own custom training algorithm and/or components.Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).Support for training single-agent, multi-agent cooperative, and multi-agentĬompetitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).Flexible Unity SDK that can be integrated into your game or custom Unity scene. ![]()
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