# Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

arXiv: https://arxiv.org/abs/2302.03963 · HTML: https://mlsanity.com/paper/2302.03963/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Learning with Combinatorial Optimization Layers: a Probabilistic Approach](https://mlsanity.com/paper/2207.13513/) (2022)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective](https://mlsanity.com/paper/1404.4391/) (2014)
- [Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1709.07032/) (2017)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [A concise guide to existing and emerging vehicle routing problem variants](https://mlsanity.com/paper/1906.06750/) (2019)
- [Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace](https://mlsanity.com/paper/2202.05118/) (2022)
- [A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem](https://mlsanity.com/paper/2105.13284/) (2021)
- [Analysis and Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2106.14827/) (2021)
- [Stochastic Model Predictive Control for Autonomous Mobility on Demand](https://mlsanity.com/paper/1804.11074/) (2018)
- [Proactive rebalancing and speed-up techniques for on-demand high capacity ridesourcing services](https://mlsanity.com/paper/1902.03374/) (2019)

## Built on by (most cited first, 14 of 14)

- [Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2304.00789/) (2023)
- [Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2312.08884/) (2023)
- [Structured Reinforcement Learning for Combinatorial Decision-Making](https://mlsanity.com/paper/2505.19053/) (2025)
- [Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2404.06975/) (2024)
- [Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems](https://mlsanity.com/paper/2407.17200/) (2024)
- [WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning](https://mlsanity.com/paper/2410.06656/) (2024)
- [Combinatorial Optimization Augmented Machine Learning](https://mlsanity.com/paper/2601.10583/) (2026)
- [Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing](https://mlsanity.com/paper/2402.03589/) (2024)
- [Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems](https://mlsanity.com/paper/2406.00868/) (2024)
- [Sobolev Training of End-to-End Optimization Proxies](https://mlsanity.com/paper/2505.11342/) (2025)
- [Optimization-Augmented Machine Learning for Vehicle Operations in Emergency Medical Services](https://mlsanity.com/paper/2503.11848/) (2025)
- [Imitation Learning for Combinatorial Optimisation under Uncertainty](https://mlsanity.com/paper/2601.05383/) (2026)
- [Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests](https://mlsanity.com/paper/2603.07422/) (2026)
- [Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application](https://mlsanity.com/paper/2606.25362/) (2026)

## Abstract (arXiv)

Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit.