基于 GAED-MADDPG 多智能体强化 学习的协作策略研究.

At present, multi-agent reinforcement learning algorithms mostly adopt frameworks that are centralized in learning and decentralized in action. These frameworks may take too long to converge or may not converge at all. In order to speed up the collective learning time of multi-agents, this paper proposed a novel multi-agent group learning strategy. It used recurrent neural network (RNN) to predict the grouping matrix of multi-agents to share the experience between them, resulting in improved learning efficiency within the multi-agents group. Meanwhile, this paper proposed the concept of information trace to remedy the problem that the agents could not share information brought by the grouping. In order to strengthen the retention of excellent experience within the group, this paper proposed the practice of delaying the death time of excellent agents in the group. Finally, the results show that, compared to MADDPG, the training time is reduced by 12% in the labyrinth experiment and by 17 % in capture the flag experiment. [ABSTRACT FROM AUTHOR]