Multi-Agent Reinforcement Learning Based on K-Means Clustering in Multi-Robot Cooperative Systems

Abstract:

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To solve the curse of dimensionality problem in multi-agent reinforcement learning, a learning method based on k-means is presented in this paper. In this method, the environmental state is represented as key state factors. The state space explosion is avoided by classifying states into different clusters using k-means. The learning rate is improved by assigning different states to existent clusters, as well as corresponding strategy. Compared to traditional Q-learning, our experimental results of the multi-robot cooperation show that our scheme improves the team learning ability efficiently. Meanwhile, the cooperation efficiency can be enhanced successfully.

Info:

Periodical:

Edited by:

Yuhang Yang, Xilong Qu, Yiping Luo and Aimin Yang

Pages:

75-80

DOI:

10.4028/www.scientific.net/AMR.216.75

Citation:

C. A. Liu et al., "Multi-Agent Reinforcement Learning Based on K-Means Clustering in Multi-Robot Cooperative Systems", Advanced Materials Research, Vol. 216, pp. 75-80, 2011

Online since:

March 2011

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Price:

$35.00

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