# Reinforcement Learning for Ridesharing: An Extended Survey

arXiv: https://arxiv.org/abs/2105.01099 · HTML: https://mlsanity.com/paper/2105.01099/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 168 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 30 of 48)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [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)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing](https://mlsanity.com/paper/2010.12001/) (2020)
- [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)
- [Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2002.05513/) (2020)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer](https://mlsanity.com/paper/1906.04452/) (2019)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2010.02068/) (2020)
- [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)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems](https://mlsanity.com/paper/1911.10641/) (2019)
- [A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01755/) (2020)

## Built on by (most cited first, 30 of 35)

- [Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach](https://mlsanity.com/paper/2108.02731/) (2021)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [A Better Match for Drivers and Riders: Reinforcement Learning at Lyft](https://mlsanity.com/paper/2310.13810/) (2023)
- [Policy Evaluation for Temporal and/or Spatial Dependent Experiments](https://mlsanity.com/paper/2202.10887/) (2022)
- [Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing](https://mlsanity.com/paper/2305.10187/) (2023)
- [An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2408.10479/) (2024)
- [HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing](https://mlsanity.com/paper/2508.04811/) (2025)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Combining Experimental and Historical Data for Policy Evaluation](https://mlsanity.com/paper/2406.00317/) (2024)
- [GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching](https://mlsanity.com/paper/2408.10286/) (2024)
- [i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance](https://mlsanity.com/paper/2401.04429/) (2024)
- [Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems](https://mlsanity.com/paper/2412.15573/) (2024)
- [DECAF: Learning to be Fair in Multi-agent Resource Allocation](https://mlsanity.com/paper/2502.04281/) (2025)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads](https://mlsanity.com/paper/2301.02593/) (2023)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences](https://mlsanity.com/paper/2407.17910/) (2024)
- [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)
- [D3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market](https://mlsanity.com/paper/2605.20036/) (2026)
- [Processing Network Controls via Deep Reinforcement Learning](https://mlsanity.com/paper/2205.02119/) (2022)
- [Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach](https://mlsanity.com/paper/2210.08659/) (2022)
- [Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach](https://mlsanity.com/paper/2312.05576/) (2023)
- [Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments](https://mlsanity.com/paper/2405.20642/) (2024)
- [Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2409.03148/) (2024)
- [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)
- [The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms](https://mlsanity.com/paper/2501.07178/) (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)

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, routing, and dynamic pricing are covered. Most of the literature has appeared in the last few years, and several core challenges are to continue to be tackled: model complexity, agent coordination, and joint optimization of multiple levers. Hence, we also introduce popular data sets and open simulation environments to facilitate further research and development. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.