Buch, Englisch, 133 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 255 g
Reihe: Synthesis Lectures on Learning, Networks, and Algorithms
Buch, Englisch, 133 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 255 g
Reihe: Synthesis Lectures on Learning, Networks, and Algorithms
ISBN: 978-3-031-59642-1
Verlag: Springer
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
Introduction.- Ridesharing.- Reinforcement Learning Prime.- Pricing & Incentives.- Online Matching.- Vehicle Repositioning.- Routing.- Ride-pooling.- Related Methods.- Open Resources.- Challenges and Opportunities.- Closing Remarks.