Robot Navigation Based on Fuzzy RL Algorithm

Authors:
Yong Duan;Baoxia Cui;Huaiqing Yang
Affiliations:
School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China 110178;School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China 110178;School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China 110178
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

Citing 3
Cited 1

Technical Note: \cal Q-Learning

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Fuzzy inference system learning by reinforcement methods

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Reinforcement learning in robotics: A survey

International Journal of Robotics Research

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Abstract

This paper focused on the problem of the autonomous mobile robot navigation under the unknown and changing environment. The reinforcement learning (RL) is applied to learn behaviors of reactive robot. T-S fuzzy neural network and RL are integrated. T-S network is used to implement the mapping from the state space to Q values corresponding with action space of RL. The problem of continuous, infinite states and actions in RL is able to be solved through the function approximation of proposed method. Finally, the method of this paper is applied to learn behaviors for the reactive robot. The experiment shows that the algorithm can effectively solve the problem of navigation in a complicated unknown environment.