@article{Yan2025, 
author = {Dapeng Yan and Qingshu Guan and Bei Ou and Bowen Yan and Hui Cao and Badong Chen},
title = {A diversified tour-driven deep reinforcement learning approach to routing for intelligent and connected vehicles},
year = {2025},
journal = {Journal of Intelligent and Connected Vehicles},
volume = {8},
number = {3},
pages = {9210064},
keywords = {vehicle routing problem (VRP), deep reinforcement learning (DRL), multihead attention, Kullback–Leibler divergence},
url = {https://www.sciopen.com/article/10.26599/JICV.2025.9210064},
doi = {10.26599/JICV.2025.9210064},
abstract = {The routing problem for intelligent and connected vehicles has garnered significant attention because of its profound theoretical implications and wide-ranging practical applications. Despite advancements, existing learning-based methods often rely on training one single policy, which inadequately explores the solution space and leads to suboptimal performance. To address this limitation, we propose a diversified tour-driven deep reinforcement learning (DT-DRL) approach for solving vehicle routing problems (VRPs) across various scales. Our approach builds on the encoder‒decoder paradigm, with the encoder utilizing a multihead attention mechanism to derive informative node embeddings and a gate aggregation block to enhance state representation. During decoding, dynamic-aware context embedding is designed to capture real-time state transitions and graph variations, thereby offering comprehensive and timely information for decision-making. To promote solution diversity and expand the search space, multiple decoders with independent parameters are employed, coupled with a Kullback–Leibler divergence-based cross-entropy loss that regularizes the generation of diversified candidate tours. We validate the proposed DT-DRL through extensive experimentation on two representative routing problems for intelligent connected vehicles, namely, the traveling salesman problem (TSP) and the capacitated VRP (CVRP). The results demonstrate that DT-DRL consistently outperforms many heuristic and DRL-based methods, achieving up to a 7.54% improvement in the optimality gap, thereby establishing its effectiveness and robustness in tackling complex routing challenges for intelligent and connected vehicles.}
}