Title :
A case-based reinforcement learning for probe robot path planning
Author :
Li, Yang ; Zonghai, Chen ; Feng, Chen
Author_Institution :
Dept of Autom., Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
This paper discusses the application of case-based learning for probe robot path planning in unknown environments. Case-based learning which makes use of past experience, is an incremental learning process. This paper proposes an algorithm of introducing reinforcement learning to case-based-reasoning, which makes full use of knowledge acquired by reinforcement learning to construct and extend the case-library. This method can enhance the adaptability of robot to unknown environments and solve the problem of case acquiring as well as poor real-time performance, high learning risk of reinforcement learning. Also, with the forget-rule, case-library can be updated in time so that efficiency of case searching and learning is increased. As the learning progressing and the case-library dynamically updated, robot´s intelligence has been greatly improved. A simulation shows the validity and feasibility of this method.
Keywords :
case-based reasoning; learning (artificial intelligence); mobile robots; path planning; case acquisition; case learning; case library; case searching; case-based reinforcement learning; forget-rule; incremental learning process; poor real-time performance; probe robot path planning; robot adaptability; unknown environments; Intelligent robots; Learning; Path planning; Probes; Robotics and automation;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1020762