This paper introduces Unity RL Playground, an open-source reinforcement learning framework built on top of Unity ML-Agents. Unity RL Playground automates the process of training mobile robots to perform various locomotion tasks such as walking, running, and jumping in simulation, with the potential for seamless transfer to real hardware. Key features include one-click training for imported robot models, universal compatibility with diverse robot configurations, multi-mode motion learning capabilities, and extreme performance testing to aid in robot design optimization and morphological evolution. The attached video can be found at https://linqi-ye.github.io/video/iros25.mp4 and the code is coming soon.

Unity 3D is a popular platform for creating and operating interactive, real-time 3D content. It is a cross-platform 3D engine and a user-friendly integrated development environment (IDE) which helps in creating games in 3D as well as applications for desktop, mobile, web and more. It consists of a number of tools for programmers as well as artists to create real-time solutions, such as films and automotive, apart from games. With a vision to maximise the transformative impact of Machine Learning for researchers and developers, Unity released the first version of Unity Machine Learning Agents Toolkit (ML-Agents) in 2017. The aim of this ML environment is to allow game developers and AI researchers to use Unity as a platform to train as well as embed intelligent agents with the help of the latest advancements in ML and AI.

Today I show you a new demo I created to teach how my player avoidance AI works with ML-Agents. The goal here is to demonstrate basic concepts by making an agent that needs to avoid colliding with the red cube, also changes in target speed are applied to show you how the agent is smart enough to apply changes on the movement frequency.

Easy tips to train your Reinforcement Learning AI with Unity3D using the ML-Agents Framework. My name is Sebastian Schuchmann, AI enthusiast from Germany and we are going to cover simple, beginner-friendly ways to improve your Machine Learning process. The Algorithm used is called PPO and was developed by OpenAI (founded by Elon Musk). After watching this video you will hopefully be able to train an Artificial Intelligence to crack your favorite game. I am very curious about what you guys will create!

The past few years have witnessed breakthroughs in reinforcement learning (RL). From the first successful use of RL by a deep learning model for learning a policy from pixel input in 2013 to the OpenAI Dexterity program in 2019, we live in an exciting moment in RL research. Consequently, we need, as RL researchers, to create more and more complex environments and Unity helps us to do that. Unity ML-Agents toolkit is a new plugin based on the game engine Unity that allows us to use the Unity Game Engine as an environment builder to train agents. From playing football, learning to walk, to jump big walls, to train a cute doggy to catch sticks, Unity ML-Agents Toolkit provides a ton of amazing pre-made environment.

A new project with planes taught to fly with reinforcement learning via Unity ML-Agents. This is still a work in progress, but the AI behavior is working amazingly.

Our two previous blog entries implied that there is a role games can play in driving the development of Reinforcement Learning algorithms. As the world's most popular creation engine, Unity is at the crossroads between machine learning and gaming. It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. As the first step in this endeavor, we are excited to introduce Unity Machine Learning Agents. Machine Learning is changing the way we expect to get intelligent behavior out of autonomous agents.