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

ZHANG RuiXian ¹YANG JiaNan ¹LIANG Ye ¹LU ShengAo ¹DONG YiFei ¹YANG BaoQing ¹ZHANG LiXian¹

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作者信息

1. School of Astronautics,Harbin Institute of Technology,Harbin 150001,China
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Abstract

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.

Key words

autonomous vehicles/navigation/reinforcement learning/smoothness/stability

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基金项目

National Natural Science Foundation of China(62225305)

National Natural Science Foundation of China(12072088)

Fundamental Research Funds for the Central Universities,China(HIT.OCEF.2022047)

Fundamental Research Funds for the Central Universities,China(H1T.BRET.2022004)

Fundamental Research Funds for the Central Universities,China(HIT.DZIJ.2023049)

State Key Laboratory of Robotics and System(HIT)(JCKY2022603C016)

Heilongjiang Touyan Team()

出版年

2024

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

参考文献量35

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