首页|Navigation for autonomous vehicles via fast-stable and smooth reinforcement learning

Navigation for autonomous vehicles via fast-stable and smooth reinforcement learning

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This paper investigates the navigation problem of autonomous vehicles based on reinforcement learning(RL)with both stability and smoothness guarantees.By introducing a data-based Lyapunov function,the stability criterion in mean cost is obtained,where the Lyapunov function has a property of fast descending.Then,an off-policy RL algorithm is proposed to train safe policies,in which a more strict constraint is exerted in the framework of model-free RL to ensure the fast convergence of policy generation,in contrast with the existing RL merely with stability guarantee.In addition,by simultaneously introducing constraints on action increments and action distribution variations,the difference between the adjacent actions is effectively alleviated to ensure the smoothness of the obtained policy,instead of only seeking the similarity of the distributions of adjacent actions as commonly done in the past literature.A navigation task of a ground differentially driven mobile vehicle in simulations is adopted to demonstrate the superiority of the proposed algorithm on the fast stability and smoothness.

autonomous vehiclesnavigationreinforcement learningsmoothnessstability

ZHANG RuiXian、YANG JiaNan、LIANG Ye、LU ShengAo、DONG YiFei、YANG BaoQing、ZHANG LiXian

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School of Astronautics,Harbin Institute of Technology,Harbin 150001,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities,ChinaFundamental Research Funds for the Central Universities,ChinaFundamental Research Funds for the Central Universities,ChinaState Key Laboratory of Robotics and System(HIT)Heilongjiang Touyan Team

6222530512072088HIT.OCEF.2022047H1T.BRET.2022004HIT.DZIJ.2023049JCKY2022603C016

2024

10.1007/s11431-023-2483-x

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(2)
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