Devin Schwab

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Deep Reinforcement Learning for Real-Robot Soccer: A Start

We have pursued research in robot soccer for many years leading to successful teams of agile mobile robots that can manipulate a ball and strategize in the presence of an adversary. Robot soccer is a complex task. In this talk, I will present our ongoing work towards the goal of using deep reinforcement learning to learn effective robot skills. We present several examples of formalism for robot skill learning and multi-robot transfer learning. The results are promising.

Devin is a 4th year PhD student at the Robotics Institute working with Manuela Veloso. His research applies deep reinforcement learning techniques to robots and multi-agent systems, such as RoboCup Small Size League soccer.

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