This paper studies the stochastic on-time arrival (SOTA) problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely sample efficient generalized actor critic (SEGAC). Different from almost all canonical SOTA solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, SEGAC offers the following appealing characteristics. SEGAC updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained SEGAC policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art SOTA algorithms in simulations across various transportation networks. We also successfully deploy SEGAC to two real metropolitan transportation networks, namely Chengdu and Beijing, using real traffic data, with satisfying results.
SEGAC: Sample Efficient Generalized Actor Critic for the Stochastic On-Time Arrival Problem
IEEE Transactions on Intelligent Transportation Systems ; 25 , 8 ; 10190-10205
01.08.2024
11380593 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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