予測を利用した強化学習エージェントによる競技型接触インタラクション

抄録

Though there are a lot of researches about Physical Human-robot Interaction (pHRI) using prediction, few researches work on inducing the opponent’s action or outwitting the opponent. We made the push-hand game environment in order to focus on generating strategic actions and tried to make reinforcement learning agents to learn these actions by adding rewards which are directly proportional to the degree of inducement (induction reward) or the degree of outwitting (outwitting reward), defined in this research. As a result, we demonstrated that the induction reward decreases the agent’s predictive error and the outwitting reward increases the opponent’s predictive error, and both of them didn’t contribute to the winning percentage.