Self-generation of reward in reinforcement learning by universal rules of interaction with the external environment

Abstract

Various studies related to machine learning have been performed. In this study, we focus on reinforcement learning, one of the methods used in machine learning. In conventional reinforcement leaning, the design of the reward function is difficult, because it is a complex and laborious task and requires expert knowledge. In previous studies, the robot learned from external sources, not autonomously. To solve this problem, we propose a method of robot learning through interactions with humans using sensor input. The reward is also generated through interactions with humans. However, the method does not require additional tasks that must be performed by the human. Therefore, the user does not need expert knowledge, and anyone can teach the robot. Our experiment confirmed that robot learning is possible through the proposed method.