Reinforcement Learning Method with Generalization Ability Developed by Using Deep Learning for Solving a Path Finding Problem

Abstract

Nowadays, deep learning and reinforcement learning have given high performance in various fields, and attracted much attention. In order to apply these learning methods to real problems, they must have a sufficient generalization ability. Whereas to improve the generalization ability has been actively studied in some fields such as image recognition and speech recognition, it has not been sufficiently studied for sequential decision-making problems such as game play and path finding. This paper proposes a reinforcement learning method with the generalization ability developed by using deep learning for a path finding problem, which is one of the sequential decision-making problems. Experimental results show that the generalization ability of the proposed method is superior to that of deep learning methods and deep reinforcement learning methods.