# Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.06723 · HTML: https://mlsanity.com/paper/2002.06723/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles](https://mlsanity.com/paper/2104.10340/) (2021)
- [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)
- [Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models](https://mlsanity.com/paper/2411.14457/) (2024)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning](https://mlsanity.com/paper/2109.04149/) (2021)
- [Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration](https://mlsanity.com/paper/2408.11751/) (2024)
- [An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination](https://mlsanity.com/paper/2504.19480/) (2025)
- [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)
- [Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems](https://mlsanity.com/paper/2502.12605/) (2025)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)

## Abstract (arXiv)

A large portion of passenger requests is reportedly unserviced, partially due to vacant for-hire drivers' cruising behavior during the passenger seeking process. This paper aims to model the multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL) approach that captures competition among multiple agents. Because the direct application of MARL to the multi-driver system under a given reward mechanism will likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with which a more desired equilibrium can be reached. To effectively solve the bilevel optimization problem with upper level as the reward design and the lower level as a multi-agent system, a Bayesian optimization (BO) algorithm is adopted to speed up the learning process. We then apply the bilevel optimization model to two case studies, namely, e-hailing driver repositioning under service charge and multiclass taxi driver repositioning under NYC congestion pricing. In the first case study, the model is validated by the agreement between the derived optimal control from BO and that from an analytical solution. With a simple piecewise linear service charge, the objective of the e-hailing platform can be increased by 8.4%. In the second case study, an optimal toll charge of 5.1 is solved using BO, which improves the objective of city planners by 7.9%, compared to that without any toll charge. Under this optimal toll charge, the number of taxis in the NYC central business district is decreased, indicating a better traffic condition, without substantially increasing the crowdedness of the subway system.