关键词:  , 多智能体系统, 覆盖控制, 强化学习, 参数共享

Abstract: In the Mars exploration mission, in order to ensure that the rover can obtain large scale surface information, the drone group carried by the rover needs to be able to effectively cover the designated area. In this paper, a coverage control method is proposed based on multi agent deep reinforcement learning that aims to maximize the coverage object area and subject to drone communication net and energy efficiency. By adopting the CRITIC parameter sharing mechanism, the training efficiency is improved. The parameter permute invariant property is obtained by utilizing the graph net. The simulation results show that the algorithm proposed in this paper is better than two baseline methods in terms of coverage area, energy efficiency, and connectivity maintenance.

Key words: multi agent system, coverage control, reinforcement learning, parameter sharing