Path Planning of Multiagent Constrained Formation through Deep Reinforcement Learning | |
Sui Zezhi1,2; Pu Zhiqiang1,2; Yi Jianqiang1,2; Tan Xiangmin1,2 | |
2018-07 | |
会议日期 | July 8-13, 2018 |
会议地点 | Rio de Janeiro, Brazil |
英文摘要 | A parallel deep Q-network (DQN) algorithm is presented for solving multiagent constrained formation path planning, where reaching destination, avoiding obstacles, and maintaining formation are simultaneously considered as independent or interactive tasks. Parallel Q-networks are utilized for each agent to sense different feature information and learn independent behavior policy. Comprehensive reward function is designed in consideration of respective requirements and interaction constraints to correctly guide the training. In order to demonstrate the effectiveness of the algorithm, we build an end-to-end model by designing a pixel game. Both training and testing are carried out in the game with double dueling DQN and the results show that the parallel deep Q-network path planner eventually complete the three tasks very well. |
会议录出版者 | Institute of Electrical and Electronics Engineers Inc |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39696] |
专题 | 自动化研究所_综合信息系统研究中心 |
作者单位 | 1.University of Chinese Academy of Sciences Beijing, 100049, China 2.Institute of Automation, Chinese Academy of Sciences Beijing, 100190,China |
推荐引用方式 GB/T 7714 | Sui Zezhi,Pu Zhiqiang,Yi Jianqiang,et al. Path Planning of Multiagent Constrained Formation through Deep Reinforcement Learning[C]. 见:. Rio de Janeiro, Brazil. July 8-13, 2018. |
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