Bai, Yu (Key Laboratory of Road and Traffic Engineering of the Ministry o), Li, Yiming (Tongji University), Xiong, Xi (Tongji University)

Multi-Armed Bandit for Stochastic Shortest Path in Mixed Autonomy

Scheduled for presentation during the Regular Session "S31c-AI-Driven Motion Prediction and Safe Control for Autonomous Systems" (FR-LA-T31), Friday, November 21, 2025, 16:20−16:40, Southport 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

In mixed-autonomy traffic networks, autonomous vehicles (AVs) are required to make sequential routing decisions under uncertainty caused by dynamic and heterogeneous interactions with human-driven vehicles (HDVs). Early-stage greedy decisions made by AVs during interactions with the environment often result in insufficient exploration, leading to failures in discovering globally optimal strategies. The exploration–exploitation balancing mechanism inherent in multi-armed bandit (MAB) methods is well-suited for addressing such problems. Based on the Real-Time Dynamic Programming (RTDP) framework, we introduce the Upper Confidence Bound (UCB) exploration strategy from the MAB paradigm and propose a novel algorithm. We establish the path-level regret upper bound under the RTDP framework, which guarantees the worst-case convergence of the proposed algorithm. Extensive numerical experiments conducted on a real-world local road network in Shanghai demonstrate that the proposed algorithm effectively overcomes the failure of standard RTDP to converge to the optimal policy under highly stochastic environments. Moreover, compared to the standard Value Iteration (VI) framework, the RTDP-based framework demonstrates superior computational efficiency. Our results highlight the effectiveness of the proposed algorithm in routing within large-scale stochastic mixed-autonomy environments.