# Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles

arXiv: https://arxiv.org/abs/1912.03341 · HTML: https://mlsanity.com/paper/1912.03341/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2110.02629/) (2021)
- [An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem](https://mlsanity.com/paper/2107.07076/) (2021)
- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [A Deep Reinforcement Learning Approach for the Meal Delivery Problem](https://mlsanity.com/paper/2104.12000/) (2021)
- [Shared learning of powertrain control policies for vehicle fleets](https://mlsanity.com/paper/2404.17892/) (2024)
- [Integrated Optimization of Automated Warehouse Operations and Last-Mile Transport for Differentiated On-Demand Delivery](https://mlsanity.com/paper/2609.19048/) (2026)

## Abstract (arXiv)

Motivated by the promising advances of deep-reinforcement learning (DRL) applied to cooperative multi-agent systems we propose a model and learning procedure to solve the Capacitated Multi-Vehicle Routing Problem (CMVRP) with fixed fleet size. Our learning procedure follows a centralized-training and decentralized-execution paradigm. We empirically test our model and showed its capability for producing near-optimal solutions through cooperative actions. In large instances, our model generates better solutions than other commonly used heuristics. Additionally, our model can solve arbitrary instances of the CMVRP without requiring re-training.