Platoon Control of Connected Vehicles via Safe Reinforcement Learning Based on Lyapunov Based Soft Actor Critic Algorithm

• Published in: Chinese Control and Decision Conference pp. 896 - 901
• Main Authors: Luo, Xiaoyuan, Hao, Tiankuo, Li, Shaobao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.05.2024
• Subjects: Benchmark testing; Connected vehicles; Costs; distributed control; Markov Decision Process; Markov decision processes; platoon control; Process control; Reinforcement learning; safe reinforcement learning (RL); Safety
• ISSN: 1948-9447
• DOI: 10.1109/CCDC62350.2024.10587774

This paper addresses the platoon control problem of connected vehicular systems subject to safety constraints. A constrained Markov Decision Process model is established. A novel safe-reinforcement-learning control method based on the Lyapunov-based Soft Actor-Critic (LSAC) algorithm is developed, where the LSAC algorithm is designed to maximize the reward while minimizing the safety cost, ensuring minimal risk of collisions. Comparisons between the safe-reinforcement-learning control via LSAC algorithm and Soft Actor-Critic (SAC) algorithm are conducted to demonstrate the effectiveness of the proposed algorithm.