Overview

This book provides a comprehensive overview of reinforcement learning for ridesharing applications. The authors first lay out the fundamentals of the ridesharing system architectures and review the basics of reinforcement learning, including the major applicable algorithms.

Research Problems and Approaches

The book describes the research problems associated with the various aspects of a ridesharing system and discusses the existing reinforcement learning approaches for solving them. The authors survey the existing research on each problem, and then examine specific case studies.

Related Methods

The book also includes a review of two methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.