The Unity AI organization is working on amazing research and products in the areas of robotics, computer vision, and machine learning. Our AI summer interns all worked on relevant, AI-related projects that actually impact our products.

While implementing your first version control system or moving to a new one can be challenging, the long-term impact is worth it. Here’s what to consider when choosing a version control system, before you commit.

We recently published two blog posts, Five ways to speed up your workflows in the Editor and Speed up your artist workflows, both based on our e-book for professional developers, 70+ tips to increase productivity with Unity 2020 LTS. In this third and final blog post in the series, we focus on workflows and the features that help programmers get more done in less time. Let’s start with how you can save compilation time when playtesting.

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 actions to complete a task on its own using observations from its environment and a task reward signal. Here’s how our interns improved our RL and ML-agents this summer.

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, we feature a recent submission to the OpenCV Spatial AI competition that showcases robotics, computer vision, reinforcement learning, and augmented reality in Unity in an impressive suite of examples.

Learn how to leverage High Definition Render Pipeline (HDRP) settings to maximize performance and achieve powerful graphics all at once.