Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning
基于强化学习的多任务联盟并行形成策略

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.