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Self-Partitioning State Space for Behavior Acquisition of Vision-Based Mobile Robots
Takayuki Nakamura and Tsukasa Ogasawara
Nara Institute of Science and Technology, Graduate School of Information Sciences, Takayama-cho 8916-5, Ikoma, Nara 630-0101, Japan
Received:October 10, 2000Accepted:December 7, 2001Published:December 20, 2001
Keywords:self-partitioning algorithm, reinforcement learning, vision-based mobile robots, soccer robots
Abstract
An input generalization problem is one of the most important in applying reinforcement learning to real robot tasks. To cope with this problem, we propose a self-partitioning state space algorithm, which can make nonuniform quantization of state space. To show that our algorithm has generalization capability, we apply our method to two tasks in which a soccer robot shoots a ball into a goal and prevent a ball from entering a goal. To show the validity of this method, the experimental results for computer simulation and a real robot are shown.
Cite this article as:T. Nakamura and T. Ogasawara, “Self-Partitioning State Space for Behavior Acquisition of Vision-Based Mobile Robots,” J. Robot. Mechatron., Vol.13 No.6, pp. 625-636, 2001.Data files:
Copyright© 2001 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.
