A Distributed Model-Free Ride-Sharing Algorithm with Pricing using Deep Reinforcement Learning

Abstract

Modern-day ride-sharing platforms leave out drivers and customers in the decision-making process of the rides in terms of vehicle-customer matching as well as pricing. We propose a model-free Distributed Pricing-based Ride-sharing with pooling (DPRS) framework with reinforcement utility functions for both customers and drivers. The framework allows (1) drivers to choose their convenient ride based on the expected reward for this ride as well as the destination locations for future rides influenced by the supply-demand computed by the Deep Q-network, (2) customers to accept or reject rides based on their preferred pricing window, timing preferences, type of the vehicle, and convenient number of people to car pool with, (3) customer to be added to the ride queue if she/he rejects the price initiated by the driver, and (4) Influencing vehicle-passenger matching and dispatching based on prices through reinforcement learning (RL). Through our simulation of multi-Agent ride-sharing with pooling platform, we show that performance of the platform significantly improved in terms of accept rate, profits of both the customers and drivers, and reduction of travel distance as well as idle time in between rides for drivers with similar profits, when compared to the state of the art ride-sharing settings that don't consider pricing strategies or potential hotspot locations.