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Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving

文献类型：期刊论文

作者Lu, Jingwei4,5; Han, Liyuan1,6; Wei, Qinglai1,6; Wang, Xiao2,3; Dai, Xingyuan1,6; Wang, Fei-Yue1,6
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2023-04-01
卷号8期号:4页码:2821-2831
ISSN号2379-8858
关键词Autonomous vehicles Decision making Path planning Training Optimal control Deep learning Complex systems Autonomous driving deep reinforcement learning deep Q-network event-triggered control parallel control
DOI10.1109/TIV.2023.3262132
通讯作者Lu, Jingwei(lujingweihh@gmail.com)
英文摘要This paper utilizes parallel control to investigate the problem of event-triggered deep reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for decision-making of autonomous driving, without training an explicit triggering condition. Based on the framework of parallel control, the developed ETDQN incorporates information of actions into the feedback and constructs a dynamic control policy. First, in the realization of the dynamic control policy, we integrate the current state and the previous action to construct the augmented state as well as the augmented Markov decision process. Meanwhile, it is shown theoretically that the goal of the developed dynamic control policy is to learn the variation rate of the action. The augmented state contains information of the current state and the previous action, which enables the developed ETDQN to directly design the immediate reward considering communication loss. Then, based on dueling double deep Q-network (dueling DDQN), we establish the augmented action-value, value, and advantage functions to directly learn the optimal event-triggered decision-making policy of autonomous driving without an explicit triggering condition. It is worth noticing that the developed ETDQN applies to various deep Q-networks (DQNs). Empirical results demonstrate that, in event-triggered control, the developed ETDQN outperforms dueling DDQN and reduces communication loss effectively.
资助项目Key Research and Development Program 2020 of Guangzhou[202007050002] ; Key-Area Research and Development Program of Guangdong Province[U1811463] ; National Natural Science Foundation of China[2020B090921003] ; Motion G, Inc. Collaborative Research Project for Modeling, Decision and Control Algorithms of Servo Drive Systems
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000994739000019
资助机构Key Research and Development Program 2020 of Guangzhou ; Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Motion G, Inc. Collaborative Research Project for Modeling, Decision and Control Algorithms of Servo Drive Systems
源URL[http://ir.ia.ac.cn/handle/173211/53417]  
专题多模态人工智能系统全国重点实验室
通讯作者Lu, Jingwei
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Qingdao Acad Intelligent Ind, Qingdao 266114, Peoples R China
3.Anhui Univ, Sch Artificial Intelligence, Hefei 230031, Peoples R China
4.Qingdao Acad Intelligent Ind, Parallel Intelligence Innovat Res Ctr, Qingdao 266109, Peoples R China
5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Jingwei,Han, Liyuan,Wei, Qinglai,et al. Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(4):2821-2831.
APA Lu, Jingwei,Han, Liyuan,Wei, Qinglai,Wang, Xiao,Dai, Xingyuan,&Wang, Fei-Yue.(2023).Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(4),2821-2831.
MLA Lu, Jingwei,et al."Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.4(2023):2821-2831.

入库方式： OAI收割

来源：自动化研究所

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