基于LCS和LS-SVM的多机器人强化学习
Multi-Robot Reinforcement Learning Based on LCS and LS-SVM
作者: 邵 杰 , 林海霞 :郑州成功财经学院信息工程系,郑州; 杜丽娟 :商丘工学院信息与电子学院,商丘;
关键词: 学习分类器; 协同最小二乘支持向量机; 强化学习; 多机器人; Learning Classifier System; LS-SVM; Reinforcement Learning; Multi-Robot
摘要:Abstract: This paper presents a multi-robot reinforcement learning method combination LCS and LS-SVM, the optimal learning strategy LS-SVM obtained as an initial rule set of LCS. LCS interact with the environment, which can quickly find the guiding rules for multi-robot reinforcement learning, provide real-time, dynamic feedback, so that multi-robot autonomously learn the optimal strategy of mutual cooperation. Algorithm analysis and simulation show that a large space for multi-robot learning, the learning speed converges slowly, uncertainties and other learning problems can get a great improvement.
文章引用: 邵 杰 , 杜丽娟 , 林海霞 (2013) 基于LCS和LS-SVM的多机器人强化学习。 人工智能与机器人研究, 2, 24-28. doi: 10.12677/AIRR.2013.21004
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