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Continuous advantage learning for minimum-time trajectory planning of autonomous vehicles

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This paper investigates the minimum-time trajectory planning problem of an autonomous ve-hicle.To deal with unknown and uncertain dynamics of the vehicle,the trajectory planning problem is modeled as a Markov decision process with a continuous action space.To solve it,we propose a continuous advantage learning(CAL)algorithm based on the advantage-value equation,and adopt a stochastic policy in the form of multivariate Gaussian distribution to encourage exploration.A shared actor-critic architecture is designed to simultaneously approximate the stochastic policy and the value function,which greatly reduces the computation burden compared to general actor-critic methods.Moreover,the shared actor-critic is up-dated with a loss function built as mean square consistency error of the advantage-value equation,and the update step is performed several times at each time step to improve data efficiency.Simulations validate the effectiveness of the proposed CAL algorithm and its better performance than the soft actor-critic algorithm.

trajectory planningcontinuous advantage learningstochastic policyshared actor-critic

Zhuo LI、Weiran WU、Jialin WANG、Gang WANG、Jian SUN

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School of Automation,Beijing Institute of Technology,Beijing 100081,China

Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China

China Academy of Launch Vehicle Technology,Intelligent Game and Decision Laboratory,Beijing 100071,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2022ZD011930261925303621730346208810162303054U20B2073

2024

10.1007/s11432-023-4059-6

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(7)