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.