Multi-robot cooperative target encirclement through learning distributed transferable policy | |
Zhang Tianle1,2; Liu Zhen2; Wu Shiguang1,2; Pu Zhiqiang1,2; Yi Jianqiang1,2 | |
2020-07-19 | |
会议日期 | July 19-24 |
会议地点 | Online |
英文摘要 | Making efficient motion decisions for a multi-robot system is a challenging problem in target encirclement with collision avoidance. Specifically, each robot with local communication has to consider cooperative target encirclement and collision avoidance simultaneously. In this paper, a distributed transferable policy network framework based on deep reinforcement learning is proposed to solve the problem of multi-robot cooperative target encirclement with collision avoidance. The proposed policy network framework is able to process the information of uncertain number of robots and obstacles, which is a desirable property for multi-robot systems. In particular, graph attention communication mechanism is adopted to model multi-robot interactions as a graph and extract cooperative information from the graph. Long short-term memory is used to accept the states of uncertain number of obstacles. In addition, a compound reward is designed to lead the training of the behavior of target encirclement with collision avoidance. Curriculum learning is implemented to speed up the process of this training. Simulation results validate the effectiveness of the proposed algorithm. Moreover, we further show that the learned policy can directly transfer to different scenarios along with good generalization. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48731] |
专题 | 综合信息系统研究中心_飞行器智能技术 |
通讯作者 | Liu Zhen |
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 | Zhang Tianle,Liu Zhen,Wu Shiguang,et al. Multi-robot cooperative target encirclement through learning distributed transferable policy[C]. 见:. Online. July 19-24. |
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