Multi-task coalition parallel formation strategy based on reinforcement learning

被引：0

作者：

Department of Computer and Information Science, Hefei University of Technology, Hefei 230009, China ^{[1
]}

不详 ^{[2
]}

机构：

[1] Department of Computer and Information Science, Hefei University of Technology

[2] Engineering Research Center of Safety Critical Industrial Measurement and Control Technology

来源：

Zidonghua Xuebao | 2008年 / 3卷 / 349-352期

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

Markov decision process; Multi-task coalition; Parallel formation; Reinforcement learning;

D O I：

10.3724/SP.J.1004.2008.00349

中图分类号：

学科分类号：

摘要：

Agent coalition is an important manner of agents' coordination and cooperation. Forming a coalition, agents can enhance their ability to solve problems and obtain more utilities. In this paper, a novel multi-task coalition parallel formation strategy is presented, and the conclusion that the process of multi-task coalition formation is a Markov decision process is testified theoretically. Moreover, reinforcement learning is used to solve agents' behavior strategy, and the process of multi-task coalition parallel formation is described. In multi-task oriented domains, the strategy can effectively and parallel form multi-task coalitions.

引用

页码：349 / 352

页数：3

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