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 to train an agent how to play soccer before finally transferring that agent to a real robot. 

Unity Machine Learning Agents Toolkit (ML-Agents) allows users to easily get started with reinforcement learning (RL) using Unity. ML-Agents gives users a variety of sample environments and model architectures that they can use to start working with RL. Users can then tune hyperparameters to experiment and improve the resulting models. All of this can happen without the user having to worry about creating a Unity environment or importing assets – and there’s no immediate need for coding. This project out of Japan by Ghelia Inc. used the ML-Agents soccer environment to train an agent to play soccer. The resulting RL model was then deployed on real Sony toio robots to play soccer. This is an exciting example of simulation-to-real-world with robotics using ML-Agents to train.

We interviewed Ghelia’s Ryo Shimizu, CEO and President; Hidekazu Furukawa, Lead Programmer for Innovation and Brand Strategy Office; and Masatoshi Uchida, Manager for Innovation Section of the Innovation and Brand Strategy Office to find out what inspired them to build this project. Read on to discover how they used ML-Agents Toolkit for training a real-world robot how to play soccer and how a golf ball fits into this scenario.

Source: Unity Technologies Blog

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