Expression of Continuous State and Action Spaces for Q-Learning Using Neural Networks and CMAC

摘要

This paper proposes a new reinforcement learning algorithm that can learn, using neural networks and CMAC, a mapping function between high-dimensional sensors and the motors of an autonomous robot. Conventional reinforcement learning algorithms require a lot of memory because they use lookup tables to describe high-dimensional mapping functions. Researchers have therefore tried to develop reinforcement learning algorithms that can learn the high-dimensional mapping functions. We apply the proposed method to an autonomous robot navigation problem and a multi-link robot arm reaching problem, and we evaluate the effectiveness of the method.