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Machine Learning

At Unity, we aim to maximize the transformative impact of Machine Learning for researchers and developers alike. Our Machine Learning tools, combined with the Unity platform, promote innovation. To further strengthen the Machine Learning community, we provide a forum where researchers and developers can exchange information, share projects, and support one another to advance the field.

Learn what Unity is up to in the area of Machine Learning.

Download ML-Agents

Understand the true potential of AI

Unity ML-Agents offers a flexible way to develop and test new AI algorithms quickly and efficiently across a new generation of robotics, games, and beyond.

Dr. Danny Lange, VP of AI and Machine Learning at Unity Technologies, former head of Machine Learning at Uber and Amazon.

Unity Machine Learning Agents beta

Unity Machine Learning Agents, the first of Unity’s machine learning product offerings, trains intelligent agents with reinforcement learning and evolutionary methods via a simple Python API, which enables:

  • Academic researchers to study complex behaviors from visual content and realistic physics
  • Industrial and enterprise researchers to implement large-scale parallel training regimes for robotics, autonomous vehicles, and other industrial applications
  • Game developers to tackle challenges, such as using agents to dynamically adjust the game-difficulty level
Download ML-Agents
Arthur Juliani, Unity Machine Learning Engineer, talks about the background and potential of Machine Learning agents.

ML-Agents Community Challenge Winners

Above each agent is a value estimate, corresponding to how much future reward the agent expects. When the right agent misses the ball, the value estimate drops to zero, since it expects the episode to end soon, resulting in no additional reward.

The rewards function

With Unity ML-Agents, a variety of training scenarios are possible, depending on how agents, brains, and rewards are connected. These include: Single Agent, Simultaneous Single Agent, Cooperative and Competitive Multi-Agent, and Ecosystem.

This tennis example shows an Adversarial Self-Play rewards function. Two interacting agents with inverse reward functions linked to a single brain. In two-player games, adversarial self-play can allow an agent to become increasingly more skilled, while always having the perfectly matched opponent: itself.

From the blog

Is your dream job at Unity?

Unity democratizes game development with tools that help developers create games and immersive experiences, work more productively, and connect with millions of users across multiple platforms.The Unity engine reaches nearly 3 billion devices worldwide.

We are currently looking for exceptional engineers to build the next-generation Machine Learning platform for Unity developers. Working closely with a stellar team of engineers and scientists, you’ll put Machine Learning and AI to work for Unity–and for the rest of the world.

See open Machine Learning jobs

Browse through our open positions in Machine Learning.

Go to Careers

Don’t see a relevant position?

We welcome unsolicited applications.

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Join Unity Machine Learning on Unity Connect

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