自动化学报2008，Vol.34Issue(3)：349-352,4.
基于强化学习的多任务联盟并行形成策略
Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning
摘要
Abstract
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.关键词
Multi-task coalition, parallel formation, Markov decision process, reinforcement learningKey words
Multi-task coalition, parallel formation, Markov decision process, reinforcement learning分类
信息技术与安全科学引用本文复制引用
蒋建国,苏兆品,齐美彬,张国富..基于强化学习的多任务联盟并行形成策略[J].自动化学报,2008,34(3):349-352,4.基金项目
Supported by National Natural Science Foundation of China (60474035), National Research Foundation for the Doctoral Program of Higher Education of China (20060359004), Natural Science Foundation of Anhui Province (070412035) （60474035）
