| 英文标题 | Research on collaborative strategy based on GAED-MADDPG multi-agent reinforcement learning |
| 作者英文名 | Zou Changjie, Zheng Jiaoling, Zhang Zhonglei |
| 机构英文名 | Software College,Chengdu University of Information Technology,Chengdu 610225,China |
| 英文摘要 | 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. |
| 英文关键词 | reinforcement learning; group collaboration; deep learning; group wisdom |