# Where to go: Agent Guidance with Deep Reinforcement Learning in A City-Scale Online Ride-Hailing Service

arXiv: https://arxiv.org/abs/2212.05742 · HTML: https://mlsanity.com/paper/2212.05742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning](https://mlsanity.com/paper/2110.02634/) (2021)
- [Efficient Neural Neighborhood Search for Pickup and Delivery Problems](https://mlsanity.com/paper/2204.11399/) (2022)
- [Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10897/) (2020)
- [Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning](https://mlsanity.com/paper/2008.05890/) (2020)
- [A Ride-Matching Strategy For Large Scale Dynamic Ridesharing Services Based on Polar Coordinates](https://mlsanity.com/paper/1906.03394/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch](https://mlsanity.com/paper/2604.25848/) (2026)

## Abstract (arXiv)

Online ride-hailing services have become a prevalent transportation system across the world. In this paper, we study a challenging problem of how to direct vacant taxis around a city such that supplies and demands can be balanced in online ride-hailing services. We design a new reward scheme that considers multiple performance metrics of online ride-hailing services. We also propose a novel deep reinforcement learning method named Deep-Q-Network with Action Mask (AM-DQN) masking off unnecessary actions in various locations such that agents can learn much faster and more efficiently. We conduct extensive experiments using a city-scale dataset from Chicago. Several popular heuristic and learning methods are also implemented as baselines for comparison. The results of the experiments show that the AM-DQN attains the best performances of all methods with respect to average failure rate, average waiting time for customers, and average idle search time for vacant taxis.