Self-Generation of Reward by Sensor Input in Reinforcement Learning

Abstract

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