MMM-PHC: A Particle-Based Multi-Agent Learning Algorithm

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI:10.1109/ICMLA.2010.15

Philip R. Cook, M. Goodrich

引用次数: 0

Abstract

Learning is one way to determine how agents should act, but learning in multi-agent systems is more difficult than in single-agent systems because other learning agents modify their behavior. We introduce a particle-based algorithm called MMM-PHC. MMM-PHC promotes convergence to Nash equilibria in matrix games using the ideas of maxim in strategies and partial commitment. Partial commitment is implemented by restricting policies to a simplex. Simulations show that MMM-PHC performs on a larger class of games than WoLFPHC.

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基于粒子的多智能体学习算法

学习是确定智能体应该如何行动的一种方法，但在多智能体系统中学习比在单智能体系统中更难，因为其他学习智能体会修改它们的行为。我们介绍了一种基于粒子的算法，称为MMM-PHC。hmm - phc利用策略的最大化和部分承诺的思想促进矩阵博弈收敛到纳什均衡。部分承诺是通过将策略限制到一个简单体来实现的。仿真表明，mm - phc比WoLFPHC在更大的游戏类别上运行。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 Ninth International Conference on Machine Learning and Applications

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