Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning  

作　　者：Junjie Wang Qichao Zhang Dongbin Zhao 

机构地区：[1]State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190 [2]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第3期567-569,共3页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(NSFC)(62173325);the Beijing Municipal Natural Science Foundation(L191002).

摘　　要：Dear editor,Deep reinforcement learning(DRL),combining the perception capability of deep learning(DL)and the decision-making capability of reinforcement learning(RL)[1],has been widely investigated for autonomous driving decision-making tasks.In this letter,Fund:supported in part by the National Natural Science Foundation of China(NSFC)(62173325);the Beijing Municipal Natural Science Foundation(L191002).

关 键 词：driving DEEP HAS 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U463.6[自动化与计算机技术—控制科学与工程]