# Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2006.10897 · HTML: https://mlsanity.com/paper/2006.10897/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch](https://mlsanity.com/paper/2501.13448/) (2025)
- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery](https://mlsanity.com/paper/2103.03450/) (2021)
- [Where to go: Agent Guidance with Deep Reinforcement Learning in A City-Scale Online Ride-Hailing Service](https://mlsanity.com/paper/2212.05742/) (2022)
- [Long-term Fairness in Ride-Hailing Platform](https://mlsanity.com/paper/2407.17839/) (2024)
- [Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives](https://mlsanity.com/paper/2510.13227/) (2025)

## Abstract (arXiv)

With the advent of ride-sharing services, there is a huge increase in the number of people who rely on them for various needs. Most of the earlier approaches tackling this issue required handcrafted functions for estimating travel times and passenger waiting times. Traditional Reinforcement Learning (RL) based methods attempting to solve the ridesharing problem are unable to accurately model the complex environment in which taxis operate. Prior Multi-Agent Deep RL based methods based on Independent DQN (IDQN) learn decentralized value functions prone to instability due to the concurrent learning and exploring of multiple agents. Our proposed method based on QMIX is able to achieve centralized training with decentralized execution. We show that our model performs better than the IDQN baseline on a fixed grid size and is able to generalize well to smaller or larger grid sizes. Also, our algorithm is able to outperform IDQN baseline in the scenario where we have a variable number of passengers and cars in each episode. Code for our paper is publicly available at: https://github.com/UMich-ML-Group/RL-Ridesharing.