Reinforcement Learning in the Ridesharing Marketplace
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Beschreibung
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. 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. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.
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. 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. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.
Explains the benefits of taking a reinforcement learning approach to ridesharing optimization problems Analyzes a number of specific works that cover the optimization of ridesharing platforms using reinforcement learning Highlights the major challenges and opportunities that are crucial for advancing reinforcement learning for ridesharing
Autor*in
Zhiwei (Tony) Qin
Themen in »Reinforcement Learning in the Ridesharing Marketplace«
Reinforcement Learning Ridesharing Dynamic Pricing Routing Vehicle Repositioning Online Matching