现代电子技术2024，Vol.47Issue(15)：102-108,7.DOI:10.16652/j.issn.1004-373x.2024.15.017
基于深度强化学习的异构智能体编队避障控制方法
Heterogeneous agent formation obstacle avoidance control method based on deep reinforcement learning
摘要
Abstract
In view of the heterogeneity of individual agents and the complexity of multi-tasks in formation obstacle avoidance control,a heterogeneous agent formation obstacle avoidance control method based on deep reinforcement learning is proposed.The local observation representations adopted by the leader and follower agents are described in detail in order to overcome the heterogeneity of individual agents.According to the corresponding tasks of the agents,three composite reward functions of formation,obstacle avoidance and navigation are designed to achieve more flexible and efficient formation obstacle avoidance control.An actor-critic network integrating attention mechanism is designed for joint training of the motion strategies of the leader and follower,so that the agents can gradually optimize the comprehensive strategy to cope with complex interaction information.Numerical simulation results show that the proposed method enables the agents to complete their respective tasks effectively.In comparison with the other reinforcement learning algorithms,the proposed method can make the agents learn the optimal motion strategy more quickly and accurately,so it has potential prospects and value for future applications in complex environments.关键词
编队避障控制/异构性/多任务/领航者-跟随者/深度强化学习/综合奖励函数/注意力机制/运动策略Key words
formation obstacle avoidance control/heterogeneity/multi-tasking/leader-follower/deep reinforcement learning/composite reward function/attention mechanism/motion strategy分类
信息技术与安全科学引用本文复制引用
虞逸凡,岳圣智,徐俊,宋婧菡,林远山..基于深度强化学习的异构智能体编队避障控制方法[J].现代电子技术,2024,47(15):102-108,7.基金项目
广西重点研发计划(桂科AB23075150) （桂科AB23075150）
设施渔业教育部重点实验室开放课题(202219) （202219）
辽宁省应用基础计划项目(2022JH2/101300187) （2022JH2/101300187）
2023中央财政对辽宁渔业补助项目 （）
