A Reinforcement Learning Model for Robots as Teachers*

Sayanti Roy, Christopher Crick, Emily Kieson, Charles Abramson

Abstract: Robots are capable of training humans to achieve complex tasks, and their helpful feedback can lead to useful human-robot collaborations. In this research we present a reinforcement learning model influenced by human cognition which is repurposed to enhance human learning, investigate a robot's ability to encourage and motivate humans and improve their performance. During teaching the robot trades off between exploration and exploitation to understand the human perception and develop a successful motivational approach. We compare our learned reinforcement model with a baseline nonreinforcement approach and with a random reinforcer, and achieve more effective teaching in the learned reinforcement condition. In addition, we discovered an extremely strong relationship (r = 0.88) between the robot's regret, in a machine learning sense, and the performance of its human partner.