Predicting and Preventing Coordination Problems in Cooperative Q-learning Systems

被引：0

作者：

Fulda, Nancy ^{[1
]}

Ventura, Dan ^{[1
]}

机构：

[1] Brigham Young Univ, Dept Comp Sci, Provo, UT 84602 USA

来源：

20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a conceptual framework for creating Q-learning-based algorithms that converge to optimal equilibria in cooperative multiagent settings. This framework includes a set of conditions that are sufficient to guarantee optimal system performance. We demonstrate the efficacy of the framework by using it to analyze several well-known multi-agent learning algorithms and conclude by employing it as a design tool to construct a simple, novel multiagent learning algorithm.

引用

页码：780 / 785

页数：6

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