| Notes |
Search Unity
Search...
Products
Solutions
Made with Unity
Learning
Support & Services
Community
Get started
Unity Machine Learning Agents
Train and embed intelligent agents by leveraging state-of-the-art deep learning technology.
Download from Github
Overview
Features
Resources
Download from Github
responsive and intelligent virtual players
Create an intelligent game experience
Creating responsive and intelligent virtual players and non-playable game characters is hard. Especially when the game is complex. To create intelligent behaviors, developers have had to resort to writing tons of code or using highly specialized tools.
With Unity Machine Learning Agents (ML-Agents), you are no longer “coding” emergent behaviors, but rather teaching intelligent agents to “learn” through a combination of deep reinforcement learning and imitation learning. Using ML-Agents allows developers to create more compelling gameplay and an enhanced game experience.
Complex AI environments
Create realistic and complex AI environments to train models
Advancement of artificial intelligence (AI) research depends on figuring out tough problems in existing environments using current benchmarks for training AI models. However, as these challenges are “solved,” the need for novel environments arises. But creating such environments is often time-intensive and requires specialized domain knowledge.
Using Unity and the ML-Agents toolkit, you can create AI environments that are physically, visually, and cognitively rich. You can use them for benchmarking as well as researching new algorithms and methods.
How Unity ML-Agents works
Integrate ML Agents
Integrate
Integrate the ML-Agents Unity package.
Train agents
Train
Connect your integrated Unity project and start training the agents to learn the right behaviour.
Puppo The Corgi
Embed
When training is complete, embed the trained agent model back into your Unity project.
Source of Madness
Millions of natural-feeling, procedurally generated monsters
In Source of Madness, an action rogue-lite game created by Carry Castle, you traverse an ever-changing dynamic world, battling new procedurally generated monsters each playthrough, brought to life by a powerful machine-learning AI.
Creating the enemy monsters involved several challenges for the small team:
The physics controlling the monsters is unusual
There are millions of variations of monsters
The monsters needed to look natural
Source of Madness game art
Using the ML-Agents toolkit – and, specifically, deep reinforcement learning – the team trained and created a neural network model that produced the right behavior. Then, using the Unity Inference Engine, they embedded the model into the game.
Check out Source of Madness
Unity and Jamcity logos
Faster training times with JamCity Snoopy Pop
We partnered with JamCity to train an agent for their bubble shooter Snoopy Pop. One of the challenges with training an agent to play Snoopy Pop is the large volume of gameplay data to learn effective behaviors and strategies. Additionally, most games in development are constantly evolving, so training times need to be reasonably fast. We introduced various features in ML-Agents like Asynchronous Environments, Generative Adversarial Imitation Learning (GAIL), and Soft Actor-Critic to solve these problems.
Read more
Key benefits
Open source
The Unity ML-Agents toolkit is open source with an Apache 2.0 license. This allows you to modify and implement ML-Agents according to your needs.
AI/ML expertise not required
The toolkit has everything you need to get started, including ready-to-use state-of-the-art algorithms and robust documentation and example projects. You also get the support of a helpful gamedev community.
Easy setup with minimal coding
It’s quick and easy to set up your game as an AI training environment. You can create intelligent characters without a lot of coding.
Plenty of starter environments
Whether your project is a 2D game, continuous control system, or large game space, several starter environments are available to help you get started.
Cross-platform inference support
Using the Unity Inference Engine (Barracuda), you can deploy your ML-Agents models on any platform (PC, mobile or console) that is supported in Unity.
Extensible training of agents
Access to C#, communication protocol, and a low-level Python API that gives you the flexibility to try different algorithms and methods for training agents enriches your advanced AI and research use cases.
DOTS ML Agents
DOTS and ML-Agents
A version of ML-Agents built for DOTS has been prototyped, and this prototype has been integrated into sample scenes and Unity demos such as MegaCity and TinyRacing. The agents were trained in complex and large environments in just a couple of hours on a standard laptop.
If your game or Unity project is being built using DOTS and you are interested in ML-Agents, please email us. We are looking for interested preview users to work with us on improving ML-Agents for DOTS.
Scale up your training
Need to scale up your training?
An ML-Agents cloud offering will be available later this year that will enable ML-Agents users to train on a scalable cloud infrastructure. With this cloud offering you will be able to submit many concurrent training sessions or easily scale out a training session across many machines for faster results.
If you are currently using ML-Agents and need help with scaling and managing experiments, sign up for a preview access.
Sign up now
Check out these ML-Agents videos
From the blog
Check out some recent highlights, or explore more posts on the blog.
ML-Agents Hummingbird Course
ML-Agents Hummingbird Course
Learn how to implement ML-Agents
We’ve partnered with Immersive Limit to create an online course that teaches you how to implement ML-Agents through exercises, code walkthroughs, and helpful discussions.
Sign up now
Tap the community
Ask questions, find answers, and connect with other Unity ML-Agents experts and experimenters, including Unity staff.
Visit Forum
Support Issues
File support issues on our github page
Learn more
Have an elaborate or complex scenario?
Contact us - ml-agents@unity3d.com
Train and embed intelligent agents
Use Unity ML-Agents and state-of-the-art deep learning technology to create complex AI environments and an intelligent game experience.
Download from Github
Language
English
Chinese
Deutsch
Español
Japanese
Russian
Français
Korean
Portuguese
Social
Purchase
Products
Subscription
Asset Store
Unity Gear
Resellers
Education
Students
Educators
Certification
Learn
Center of Excellence
Download
Unity
Beta Program
Unity Labs
Labs
Publications
Resources
Learn platform
Community
Documentation
Unity QA
FAQ
Services Status
Connect
Unity
Our Company
Blog
Events
Careers
Contact
Press
Partners
Affiliates
Security
Social Impact
Copyright © 2020 Unity Technologies
Legal Privacy Policy Cookies Do Not Sell My Personal Information Cookies Settings
"Unity", Unity logos, and other Unity trademarks are trademarks or registered trademarks of Unity Technologies or its affiliates in the U.S. and elsewhere (more info here). Other names or brands are trademarks of their respective owners. |