Multi-Agent Formation Control with Obstacles Avoidance under Restricted Communication through Graph Reinforcement Learning | |
Huimu, Wang1,2; Tenghai, Qiu1; Zhen, Liu1; Zhiqiang, Pu1,2; Jianqiang, Yi1,2 | |
2020 | |
会议日期 | 2020.06 |
会议地点 | 线上 |
英文摘要 | Multi-agent formation control with obstacles avoidance (MAFC-OA) is one of the attractive tasks of multi-agent cooperation. Although a number of algorithms can achieve formation control effectively, they ignore the nature structure feature of the graph formed by agents. Given this problem, a model, MAFC-OA, which is composed of observation attention network, action attention network and Multi-long short-term memory (Multi-LSTM) is proposed. With MAFC-OA, the agents can be trained to form the desired formation and avoid dynamic obstacles in the environments with restricted communication. Specifically, the above two attention networks not only incorporate the influence of the nearby agents’ observation and actions, but also enlarge the agents’ receptive field (communication range) through the chain propagation characteristics to promote cooperation among agents. Moreover, the Multi-LSTM allows the agents to take obstacles into consideration in the order of distance and to avoid the obstacles effectively. Simulations demonstrate that the agents can form the desired formation and avoid dynamic obstacles effectively.
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产权排序 | 1 |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44951] |
专题 | 综合信息系统研究中心_飞行器智能技术 |
通讯作者 | Tenghai, Qiu |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Huimu, Wang,Tenghai, Qiu,Zhen, Liu,et al. Multi-Agent Formation Control with Obstacles Avoidance under Restricted Communication through Graph Reinforcement Learning[C]. 见:. 线上. 2020.06. |
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