Enhancing Learning Capabilities by XCS with Best Action Mapping
Enhancing Learning Capabilities by XCS with Best Action Mapping
复制标题
通过 XCS 通过最佳动作映射增强学习能力
DOI：
10.1007/978-3-642-32937-1_26
复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
K. Takadama
中科院分区：
文献类型：
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作者：
Masaya Nakata;P. Lanzi;K. Takadama
本文提出了一种新的XCS的方法，称为XCS与最佳动作映射（XCSB），以提高XCS的学习能力。XCSB的特点是只学习具有最高预测收益的最佳动作，而XCS学习具有最高和最低预测收益的动作，具有高精度。为了研究XCSB的有效性，我们将XCSB应用于两个基准问题：作为单步问题的多路复用器问题和作为多步问题的迷宫问题。实验结果表明：（1）XCSB算法能够快速地解决状态空间较大的问题;（2）XCSB算法能够以较小的最大种群规模获得较高的性能。
This paper proposes a novel approach of XCS called XCS with Best Action Mapping (XCSB) to enhance the learning capabilities of XCS. The feature of XCSB is to learn onlybest actionshaving thehighestpredicted payoff with the high accuracy unlike XCS which learns actions having thehighestandlowestpredicted payoff with the high accuracy. To investigate the effectiveness of XCSB, we applied XCSB to two benchmark problems: multiplexer problem as a single step problem and maze problem as a multi step problem. The experimental results show that (1) XCSB can solve quickly the problem which has a large state space and (2) XCSB can achieve a high performance with a small max population size.