Real-Time Interactive Reinforcement Learning for Robots

Andrea Lockerd Thomaz, Guy Hoffman, and Cynthia Breazeal

It is our goal to understand the role real-time human interaction can play in machine learning algorithms for robots. In this paper we present Interactive Reinforcement Learning (IRL) as a plausible approach for training human-centric assistive robots by natural interaction. We describe an experimental platform to study IRL, pose questions arising from IRL, and discuss initial observations obtained during the development of our system.


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