Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning

代理人联盟是代理人协作和合作的一种重要方式。形成一个联盟，代理人能提高他们的能力解决问题并且获得更多的实用程序。在这份报纸，新奇多工联盟平行形成策略被介绍，并且多工联盟形成的过程是一个 Markov 决定过程的结论理论上被证明。而且，学习的加强被用来解决多工联盟平行的代理人行为策略，和这个过程形成被描述。在多工面向的领域，策略罐头有效地并且平行形式多工联盟。

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.