# DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning

## TL;DR

DeepPool is a distributed deep reinforcement learning algorithm that optimizes ride-sharing dispatching, improving efficiency and adaptability in dynamic environments by learning from real-world data.

## Contribution

It introduces a novel distributed model-free deep Q-network approach for ride-sharing dispatching that incorporates demand forecasting and operates without centralized coordination.

## Key findings

- Outperforms existing strategies on NYC taxi data
- Effectively manages ride sharing and vehicle dispatching
- Adapts quickly to changing demand environments

## Abstract

The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed. Further, ride-sharing allows sharing costs and, hence, reduces the congestion and emission by making better use of vehicle capacities. In this work, we develop a distributed model-free, DeepPool, that uses deep Q-network (DQN) techniques to learn optimal dispatch policies by interacting with the environment. Further, DeepPool efficiently incorporates travel demand statistics and deep learning models to manage dispatching vehicles for improved ride sharing services. Using real-world dataset of taxi trip records in New York City, DeepPool performs better than other strategies, proposed in the literature, that do not consider ride sharing or do not dispatch the vehicles to regions where the future demand is anticipated. Finally, DeepPool can adapt rapidly to dynamic environments since it is implemented in a distributed manner in which each vehicle solves its own DQN individually without coordination.