# Reinforcement Learning for Solving Stochastic Vehicle Routing Problem

arXiv: https://arxiv.org/abs/2311.07708 · HTML: https://mlsanity.com/paper/2311.07708/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)

## Built on by (most cited first, 3 of 3)

- [SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem](https://mlsanity.com/paper/2505.21887/) (2025)
- [Solving the Min-Max Multiple Traveling Salesmen Problem via Learning-Based Path Generation and Optimal Splitting](https://mlsanity.com/paper/2508.17087/) (2025)
- [End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW](https://mlsanity.com/paper/2512.01518/) (2025)

## Abstract (arXiv)

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.