# Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network

arXiv: https://arxiv.org/abs/2102.06854 · HTML: https://mlsanity.com/paper/2102.06854/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Multi-Agent Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1907.13220/) (2019)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective](https://mlsanity.com/paper/1404.4391/) (2014)
- [Computational Rationalization: The Inverse Equilibrium Problem](https://mlsanity.com/paper/1308.3506/) (2013)
- [MOVI: A Model-Free Approach to Dynamic Fleet Management](https://mlsanity.com/paper/1804.04758/) (2018)
- [Decentralised Learning in Systems with Many, Many Strategic Agents](https://mlsanity.com/paper/1803.05028/) (2018)
- [Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games](https://mlsanity.com/paper/1403.6508/) (2014)
- [Multi-Agent Routing Value Iteration Network](https://mlsanity.com/paper/2007.05096/) (2020)
- [Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems](https://mlsanity.com/paper/1901.10923/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts](https://mlsanity.com/paper/2109.01178/) (2021)
- [Stage Light is Sequence2: Multi-Light Control via Imitation Learning](https://mlsanity.com/paper/2605.03660/) (2026)

## Abstract (arXiv)

Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data of riders and drivers and optimize supply and demand matching in real time. While these mobility service providers have some degree of control over the market by assigning vehicles to requests, they need to deal with the uncertainty arising from self-interested driver behavior since workers are usually free to drive when they are not assigned tasks. In this work, we formulate the problem of passenger-vehicle matching in a sparsely connected graph and proposed an algorithm to derive an equilibrium policy in a multi-agent environment. Our framework combines value iteration methods to estimate the optimal policy given expected state visitation and policy propagation to compute multi-agent state visitation frequencies. Furthermore, we developed a method to learn the driver's reward function transferable to an environment with significantly different dynamics from training data. We evaluated the robustness to changes in spatio-temporal supply-demand distributions and deterioration in data quality using a real-world taxi trajectory dataset; our approach significantly outperforms several baselines in terms of imitation accuracy. The computational time required to obtain an equilibrium policy shared by all vehicles does not depend on the number of agents, and even on the scale of real-world services, it takes only a few seconds on a single CPU.