# Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.04555 · HTML: https://mlsanity.com/paper/2103.04555/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1709.07032/) (2017)
- [MOVI: A Model-Free Approach to Dynamic Fleet Management](https://mlsanity.com/paper/1804.04758/) (2018)
- [CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms](https://mlsanity.com/paper/1905.11353/) (2019)
- [Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach](https://mlsanity.com/paper/1603.04418/) (2016)
- [Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning](https://mlsanity.com/paper/1905.09906/) (2019)
- [Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1509.03985/) (2015)
- [Predictive Positioning and Quality Of Service Ridesharing for Campus Mobility On Demand Systems](https://mlsanity.com/paper/1609.08116/) (2016)

## Built on by (most cited first, 22 of 22)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2302.03963/) (2023)
- [Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace](https://mlsanity.com/paper/2202.05118/) (2022)
- [Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2312.08884/) (2023)
- [i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance](https://mlsanity.com/paper/2401.04429/) (2024)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System](https://mlsanity.com/paper/2401.00093/) (2023)
- [Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2404.06975/) (2024)
- [Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework](https://mlsanity.com/paper/2501.14199/) (2025)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06742/) (2023)
- [DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning](https://mlsanity.com/paper/2109.04149/) (2021)
- [A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency](https://mlsanity.com/paper/2302.14358/) (2023)
- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization](https://mlsanity.com/paper/2105.13461/) (2021)
- [Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions](https://mlsanity.com/paper/2111.03204/) (2021)
- [SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing](https://mlsanity.com/paper/2411.18432/) (2024)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)
- [Ride-pool Assignment Algorithms: Modern Implementation and Swapping Heuristics](https://mlsanity.com/paper/2504.10649/) (2025)
- [A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch](https://mlsanity.com/paper/2604.25848/) (2026)

## Abstract (arXiv)

We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms. Our approach learns the spatiotemporal state-value function using a batch training algorithm with deep value networks. The optimal repositioning action is generated on-demand through value-based policy search, which combines planning and bootstrapping with the value networks. For the large-fleet problems, we develop several algorithmic features that we incorporate into our framework and that we demonstrate to induce coordination among the algorithmically-guided vehicles. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency meausred by income-per-hour. We have also designed and run a real-world experiment program with regular drivers on a major ride-hailing platform. We have observed significantly positive results on key metrics comparing our method with experienced drivers who performed idle-time repositioning based on their own expertise.