September 30, 2021
The ML-Agents Toolkit allows researchers and game developers to build and train agents in Unity environments using Reinforcement Learning (RL). RL is useful when it is easier to specify what task an agent needs to complete rather than how to do it. An agent learns to select ...
August 6, 2021
Our Made with Unity: AI series showcases Unity projects made by creators for a range of purposes that involve our artificial intelligence products. In this example, ML-Agents empowered AI developers by allowing them to quickly and easily set up machine learning environments and t...
July 12, 2021
We are excited to share a new open-source environment to further demonstrate what ML-Agents can do. DodgeBall is a competitive team vs. team shooter-like environment where agents compete in rounds of Elimination or Capture-the-Flag.
May 5, 2021
Today, we’re delighted to announce the v2.0 release of the ML-Agents Unity package, currently on track to be verified for the 2021.2 Editor release.
December 28, 2020
As we close out 2020, we wanted to take a moment to highlight a few of our favorite community projects in 2020, recap our progress since our v1.0 release (Release 1) in April 2020, and provide an overview of what’s in store for 2021.
December 11, 2020
It’s easy to automate playtesting by creating a Virtual Player (a game-playing agent), then using Game Simulation to run automated playtests with your Virtual Player at scale.
November 20, 2020
Within Eidos Labs, several projects use machine learning. The Automated Game Testing project tackles the problem of testing the functionality of expansive AAA games by modeling player behavior with agents that have learned behavior using reinforcement learning (RL). In this...
November 11, 2020
Each summer, interns join AI@Unity to develop highly impactful technology that forwards our mission to empower Unity developers with Artificial Intelligence and Machine Learning tools and services. This past summer was no different, and the AI@Unity group was delighted to have 24...
March 6, 2020
Our newest additions to the Unity Learn platform will teach you how to use Reinforcement Learning and AI to solve game development challenges and make better, smarter games.
February 28, 2020
In the latest release of the ML-Agents Toolkit (v0.14), we have added a self-play feature that provides the capability to train competitive agents in adversarial games (as in zero-sum games, where one agent’s gain is exactly the other agent’s loss). In this blog post, we provide ...
November 29, 2019
In a few short weeks, Unity will be heading to NeurIPS in Vancouver (December 8–14). We’re sponsoring the main conference and the Women in Machine Learning (WiML) Workshop, as well as co-organizing the NeurIPS 2019 Workshop on Learning Transferable Skills. Learning transferable s...
November 11, 2019
In v0.9 and v0.10 of ML-Agents, we introduced a series of features aimed at decreasing training time, namely Asynchronous Environments, Generative Adversarial Imitation Learning (GAIL), and Soft Actor-Critic. With our partner JamCity, we previously showed that the parallel Unity ...
