mlsanityData to 14 Oct 2026

Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding

arXiv 2601.15131 ↗First posted 21 Jan 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

In this paper, we study the vehicle routing problem with a finite time horizon. In this routing problem, the objective is to maximize the number of customer requests served within a finite time horizon. We present a novel routing network embedding module which creates local node embedding vectors and a context-aware global graph representation. The proposed Markov decision process for the vehicle routing problem incorporates the node features, the network adjacency matrix and the edge features as components of the state space. We incorporate the remaining finite time horizon into the network embedding module to provide a proper routing context to the embedding module. We integrate our embedding module with a policy gradient-based deep Reinforcement Learning framework to solve the vehicle routing problem with finite time horizon. We trained and validated our proposed routing method on real-world routing networks, as well as synthetically generated Euclidean networks. Our experimental results show that our method achieves a higher customer service rate than the existing routing methods. Additionally, the solution time of our method is significantly lower than that of the existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 30 references that are in the map. First posted on arXiv 21 Jan 2026.

Builds on

5 of 30 references.

References · 5

  1. 2017Graph Attention Networkscited by 8,923
  2. 2018Attention, Learn to Solve Routing Problems!cited by 656
  3. 2020Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windowscited by 25
  4. 2020A Reinforcement Learning Approach to the Orienteering Problem with Time Windowscited by 8
  5. 2022Solving Large-Scale Dynamic Vehicle Routing Problems with Stochastic Requestscited by 3

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  4. 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
  5. 2025EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems
  6. 2026Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
  7. 2024Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
  8. 2026Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry