# Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

arXiv: https://arxiv.org/abs/2312.15195 · HTML: https://mlsanity.com/paper/2312.15195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [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)
- [Quantifying the benefits of vehicle pooling with shareability networks](https://mlsanity.com/paper/1310.2963/) (2013)
- [Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling](https://mlsanity.com/paper/2302.10510/) (2023)

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

- [TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents](https://mlsanity.com/paper/2404.12530/) (2024)
- [Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?](https://mlsanity.com/paper/2510.03257/) (2025)
- [PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning](https://mlsanity.com/paper/2512.07342/) (2025)

## Abstract (arXiv)

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft). Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), in which the regional dispatching of vehicles has a significant impact on the matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get a ride. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and order distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3% over the existing best on-demand ride pooling method.