Multi-agent Collaborative Learning with Relational Graph Reasoning in Adversarial Environments | |
Wu Shiguang1,2; Qiu Tenghai2; Pu Zhiqiang1,2; Yi Jianqiang1,2 | |
2021-09-27 | |
会议日期 | 2021-9 |
会议地点 | 线上会议 |
英文摘要 | This paper proposes a collaborative policy frame work via relational graph reasoning for multi-agent systems to accomplish adversarial tasks. A relational graph reasoning module consisting of an agent graph reasoning module and an opponent graph module, is designed to enable each agent to learn mixture state representation to enhance the effectiveness of the policy. In particular, for each agent, the agent graph reasoning module is designed to infer different underlying influences from different opponents and generate agent-level state representation. The opponent graph reasoning module is creatively designed for the opponents to reason relations from their surrounding objects including the agents and the opponents based on their latent features and then predict the future state of the opponents. It forms an opponent-level state representation. Besides, in order to effectively predict the state of the opponents, an intrinsic reward based on prediction error is designed to motivate the policy learning. Furthermore, interactions among agents are utilized to transmit messages and fuse information to promote the cooperative behaviors among the agents. Finally, various representative simulations on two multi-agent adversarial tasks are conducted to demonstrate the superiority and effectiveness of the proposed framework by comparison with existing methods. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48728] |
专题 | 综合信息系统研究中心_飞行器智能技术 |
通讯作者 | Qiu Tenghai |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Wu Shiguang,Qiu Tenghai,Pu Zhiqiang,et al. Multi-agent Collaborative Learning with Relational Graph Reasoning in Adversarial Environments[C]. 见:. 线上会议. 2021-9. |
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