Reinforcement Learning for Demand Forecasting and Customized Services

Chapter 6 Reinforcement Learning for Demand Forecasting and Customized Services Sini Raj Pulari, Sini Raj Pulari Bahrain Polytechnic, ISA Town, BahrainSearch for more papers by this authorT. S. Murugesh, T. S. Murugesh Department of Electronics and Communication Engineering, Government College of Engineering Srirangam, Tiruchirappalli, Tamil Nadu, India (On Deputation from Annamalai University, Department of Electronics and Instrumentation Engineering, Faculty of Engineering & Technology, India)Search for more papers by this authorShriram K. Vasudevan, Shriram K. Vasudevan Lead Technical – Evangelist (Asia Pacific and Japan), Intel India Pvt. Ltd., Bengaluru, Karnataka, IndiaSearch for more papers by this authorAkshay Bhuvaneswari Ramakrishnan, Akshay Bhuvaneswari Ramakrishnan Department of Computer Science and Engineering, Sastra Deemed to be University, SASTRA University Thanjavur Campus, Thanjavur, Tamil Nadu, IndiaSearch for more papers by this author Sini Raj Pulari, Sini Raj Pulari Bahrain Polytechnic, ISA Town, BahrainSearch for more papers by this authorT. S. Murugesh, T. S. Murugesh Department of Electronics and Communication Engineering, Government College of Engineering Srirangam, Tiruchirappalli, Tamil Nadu, India (On Deputation from Annamalai University, Department of Electronics and Instrumentation Engineering, Faculty of Engineering & Technology, India)Search for more papers by this authorShriram K. Vasudevan, Shriram K. Vasudevan Lead Technical – Evangelist (Asia Pacific and Japan), Intel India Pvt. Ltd., Bengaluru, Karnataka, IndiaSearch for more papers by this authorAkshay Bhuvaneswari Ramakrishnan, Akshay Bhuvaneswari Ramakrishnan Department of Computer Science and Engineering, Sastra Deemed to be University, SASTRA University Thanjavur Campus, Thanjavur, Tamil Nadu, IndiaSearch for more papers by this author Book Editor(s):R. Elakkiya, R. Elakkiya Department of Computer Science, Birla Institute of Technology & Science Pilani, Dubai Campus, UAESearch for more papers by this authorV. Subramaniyaswamy, V. Subramaniyaswamy School of Computing, SASTRA Deemed University, Thanjavur, IndiaSearch for more papers by this author First published: 12 April 2024 https://doi.org/10.1002/9781394214068.ch6 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Reinforcement Learning (RL) is a strong way for machines to learn that has shown promise in areas like predicting demand and providing personalized services. This chapter investigates how strategies based on RL can improve the accuracy of demand forecasting and make it possible for businesses to provide individualized services to each of its clients. The principles of RL, its use in demand forecasting, and the implementation of individualized services are covered extensively as key components of the topic. A real-life case study of a large retail chain highlights the practical benefits of applying RL in optimizing inventory management and providing individualized product recommendations. Researchers at the University of California, Berkeley, carried out this case study. RL gives organizations the ability to dynamically respond to shifting market dynamics and the tastes of their customers by empowering them to continuously learn and adapt to new circumstances. This gives them an advantage over their rivals. This chapter offers light on the revolutionary influence that RL has had and presents a data-driven strategy to meet the demands of modern business environments. 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