# Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

arXiv: https://arxiv.org/abs/1911.11260 · HTML: https://mlsanity.com/paper/1911.11260/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 29 of 29)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem](https://mlsanity.com/paper/2107.07076/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [A Better Match for Drivers and Riders: Reinforcement Learning at Lyft](https://mlsanity.com/paper/2310.13810/) (2023)
- [An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2408.10479/) (2024)
- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey](https://mlsanity.com/paper/2108.04462/) (2021)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2404.06975/) (2024)
- [Robo-taxi Fleet Coordination at Scale via Reinforcement Learning](https://mlsanity.com/paper/2504.06125/) (2025)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach](https://mlsanity.com/paper/2307.07508/) (2023)
- [Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning](https://mlsanity.com/paper/2311.05780/) (2023)
- [Reinforcement Learning for Assignment Problem with Time Constraints](https://mlsanity.com/paper/2106.02856/) (2021)
- [DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning](https://mlsanity.com/paper/2109.04149/) (2021)
- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems](https://mlsanity.com/paper/2510.10644/) (2025)
- [Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing](https://mlsanity.com/paper/2510.26089/) (2025)
- [dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System](https://mlsanity.com/paper/2106.05737/) (2021)
- [Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment](https://mlsanity.com/paper/2203.03021/) (2022)
- [Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach](https://mlsanity.com/paper/2210.08659/) (2022)
- [Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility](https://mlsanity.com/paper/2312.10809/) (2023)
- [A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation](https://mlsanity.com/paper/2403.03643/) (2024)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)
- [To Start Up a Start-Up-Embedding Strategic Demand Development in Operational On-Demand Fulfillment via Reinforcement Learning with Information Shaping](https://mlsanity.com/paper/2504.05633/) (2025)

## Abstract (arXiv)

Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an end-to-end machine learning method. Previous works have explored machine learning methods to the problem from a high-level perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simplification, the drivers are considered independent agents maximizing their own reward functions. In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems. In addition to treating the drivers as individual agents, we consider the problem from a system-centric perspective, where a central fleet management agent is responsible for decision-making for all drivers.