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JACIII Vol.15 No.7 pp. 822-930
doi: 10.20965/jaciii.2011.p0822
(2011)

Paper:

Network Parameter Setting for Reinforcement Learning Approaches Using Neural Networks

Kazuaki Yamada

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

Received:
March 20, 2011
Accepted:
May 9, 2011
Published:
September 20, 2011
Keywords:
reinforcement learning, artificial neural networks, autonomous mobile robot
Abstract

Reinforcement learning approaches are attracting attention as a technique for constructing a trial-anderror mapping function between sensors and motors of an autonomous mobile robot. Conventional reinforcement learning approaches use a look-up table to express the mapping function between grid state and grid action spaces. The grid size greatly adversely affects the learning performance of reinforcement learning algorithms. To avoid this, researchers have proposed reinforcement learning algorithms using neural networks to express the mapping function between continuous state space and action. A designer, however, must set the number of middle neurons and initial values of weight parameters appropriately to improve the approximate accuracy of neural networks. This paper proposes a new method that automatically sets the number ofmiddle neurons and initial values of weight parameters based on the dimension number of the sensor space. The feasibility of proposed method is demonstrated using an autonomous mobile robot navigation problem and is evaluated by comparing it with two types of Q-learning as follows: Q-learning using RBF networks and Q-learning using neural networks whose parameters are set by a designer.

Cite this article as:
K. Yamada, “Network Parameter Setting for Reinforcement Learning Approaches Using Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.7, pp. 822-930, 2011.
Data files:
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