JRM Vol.24 No.2 pp. 330-339
doi: 10.20965/jrm.2012.p0330


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

Kazuaki Yamada

Department of Mechanical Engineering, Toyo University, 2100 Kujirai, Kawagoe-shi, Saitama 350-8585, Japan

October 1, 2011
January 18, 2012
April 20, 2012
reinforcement learning, neural networks, CMAC, griddy Gibbs sampler, autonomous robots
This paper proposes a new reinforcement learning algorithm that can learn, using neural networks and CMAC, a mapping function between highdimensional 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.
Cite this article as:
K. Yamada, “Expression of Continuous State and Action Spaces for Q-Learning Using Neural Networks and CMAC,” J. Robot. Mechatron., Vol.24 No.2, pp. 330-339, 2012.
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