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

TL;DR

This paper introduces a reinforcement learning framework for vehicle dispatching in ride pooling services, utilizing mutual information as an intrinsic reward to improve matching efficiency and increase revenue.

Contribution

It proposes a novel RL-based dispatching method that incorporates mutual information as an intrinsic reward to better match vehicle and request distributions.

Findings

Revenue increased by up to 3% over existing methods

Mutual information improves request-vehicle distribution correlation

Framework effectively handles unusual request distributions

Abstract

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.