Learning Sequences of Compatible Actions Among Agents

作者:Faruk Polat, Osman Abul

摘要

Action coordination in multiagent systemsis a difficult task especially in dynamicenvironments. If the environment possessescooperation, least communication,incompatibility and local informationconstraints, the task becomes even moredifficult. Learning compatible action sequencesto achieve a designated goal under theseconstraints is studied in this work. Two newmultiagent learning algorithms called QACE andNoCommQACE are developed. To improve theperformance of the QACE and NoCommQACEalgorithms four heuristics, stateiteration, means-ends analysis, decreasing reward and do-nothing, aredeveloped. The proposed algorithms are testedon the blocks world domain and the performanceresults are reported.

论文关键词:bucket brigade learning, multiagent learning, multiagent systems, Q-learning, reinforcement learning

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论文官网地址:https://doi.org/10.1023/A:1015009422110