Multi-agent learning dynamics: A survey

被引：5

作者：

van den Herik, H. Jaap ^{[1
]}

Hennes, D. ^{[1
]}

Kaisers, M. ^{[1
]}

Tuyls, K. ^{[1
]}

Verbeeck, K. ^{[1
]}

机构：

[1] Maastricht Univ, MICC, Adapt Agents Grp, Maastricht, Netherlands

来源：

COOPERATIVE INFORMATION AGENTS XI, PROCEEDINGS | 2007年 / 4676卷

关键词：

D O I：

10.1007/978-3-540-75119-9_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration and policy iteration. Four characteristics are studied: initial conditions, parameter settings, convergence speed, and local versus global convergence. Global convergence is still difficult to achieve in practice, despite existing theoretical guarantees. Multiple visualizations are included to provide a comprehensive insight into the learning dynamics.

引用

页码：36 / +

页数：3

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