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Tube-based robust reinforcement learning for autonomous maneuver decision for UCAVs

Chinese Journal of Aeronautics 2024年第7期37卷 391-405页

作者： Lixin WANG Sizhuang ZHENG Haiyin PIAO Changqian LU Ting YUE Hailiang LIU School of Aeronautical Science and Engineering Beihang UniversityBeijing 100191China Shenyang Aircraft Design&Research Institute Shenyang 110035China

reinforcement learning(RL)algorithms enhance intelligence of air combat AutonomousManeuver Decision(AMD)policy,but they may underperform in target combat environmentswith *** enhance the robustness of the AMD strategy learned by RL,thisstudy proposes a Tube-based robust RL(TRRL)***,this study introduces a tube todescribe reachable trajectories under disturbances,formulates a method for calculating tubes basedon sum-of-squares programming,and proposes the TRRL algorithm that enhances robustness byutilizing tube size as a quantitative ***,this study introduces offline techniques forregressing the tube size function and establishing a tube library before policy learning,aiming toeliminate complex online tube solving and reduce the computational burden during ***,an analysis of the tube library demonstrates that the mitigated AMD strategy achievesgreater robustness,as smaller tube sizes correspond to more cautious *** finding highlightsthat TRRL enhances robustness by promoting a conservative *** effectively balanceaggressiveness and robustness,the proposed TRRL algorithm introduces a“laziness factor”as aweight of ***,combat simulations in an environment with disturbances confirm thatthe AMD policy learned by the TRRL algorithm exhibits superior air combat performance comparedto selected robust RL baselines.

关键词： Air combat Autonomous maneuver decision robust reinforcement learning Tube-based algorithm Combat simulation

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Curricular robust reinforcement learning via GAN-Based Perturbation Through Continuously Scheduled Task Sequence

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Tsinghua Science and Technology 2023年第1期28卷 27-38页

作者： Yike Li Yunzhe Tian Endong Tong Wenjia Niu Yingxiao Xiang Tong Chen Yalun Wu Jiqiang Liu Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing 100044China

reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as cooperative Boston Dynamics ***,robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real *** efforts have been made to approach this problem,such as performing random environmental perturbations in the learning ***,one cannot guarantee to train with a positive perturbation as bad ones might bring failures to *** this work,we treat robust RL as a multi-task RL problem,and propose a curricular robust RL *** first present a generative adversarial network(GAN)based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current ***,with these progressive tasks,we can realize curricular learning and finally obtain a robust *** experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.

关键词： robust reinforcement learning generative adversarial network(GAN)based model curricular learning

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Towards Safe Autonomous Driving:Decision Making with Observation‑robust reinforcement learning

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Automotive Innovation 2023年第4期6卷 509-520页

作者： Xiangkun He Chen Lv State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchunChina School of Mechanical and Aerospace Engineering Nanyang Technological University639798 NanyangSingapore

Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous *** address these issues and further improve safety,automated driving is required to be capable of handling perception ***,this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous ***,an adversarial agent is trained online to generate optimal adversarial attacks on observations,which attempts to amplify the average variation distance on perturbed *** addition,an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain ***,the safe decision making scheme is evaluated on a lane change task under complex highway traffic *** results show that the developed approach can ensure autonomous driving performance,as well as the policy robustness against adversarial attacks on observations.

关键词： Autonomous vehicle robust reinforcement learning Safe decision making Adversarial attack

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