Reinforcement Learning for the Transportation Industry
Description
This book provides a comprehensive exploration of reinforcement learning and its transformative applications in transportation systems. Reinforcement Learning for the Transportation Industry begins with the technical foundations of RL, covering core architectures, formal frameworks, and major algorithms such as Q-learning, Policy Gradient, Actor-Critic, Deep Q-Networks (DQN), and Multi-Agent Reinforcement Learning (MARL). The book further examines Deep Reinforcement Learning (DRL), Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning from AI Feedback (RLAIF), and Reinforcement Fine-Tuning (RFT), highlighting their growing role in intelligent decision-making and large language models.
The later chapters focus on real-world transportation applications, including autonomous vehicles, electric vehicle routing, traffic signal coordination, traffic congestion reduction, ridesharing, transport logistics, advanced air mobility, intelligent transportation systems, and Internet of Vehicles (IoVs). Special attention is given to AutoRL, Federated Reinforcement Learning, and LLM-guided DRL for autonomous driving. By combining theoretical foundations with practical case studies, this book serves as a valuable resource for researchers, academicians, and industry professionals seeking to implement advanced RL solutions for efficient, sustainable, and intelligent transportation systems.
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Persons
Dr. Pethuru Raj, PhD, is the principal AI architect at Infocion Inc., Bangalore. Previously, he worked as the vice president and chief architect at the AI division of Reliance Jio Platforms Ltd., Bangalore, IBM Global Cloud Centre of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has gained over 25 years of experience in the IT industry and nine years of research experience. He has finished the CSIR-sponsored PhD at Anna University, Chennai. He continued with UGC-sponsored postdoctoral research in the Department of Computer Science and Automation at the Indian Institute of Science (IISc), Bangalore. After that, he got two international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities.
Dr. B. Sundaravadivazhagan obtained his PhD in computer science from Anna University, Chennai India. Currently, he is on the faculty in the Department of Information Technology at the University of Technology and Applied Sciences-AL Mussanah in Oman He has professional memberships in ISACA and ISTE and is a senior member of IEEE. His academic and research background spans more than 23 years at many institutions. Working on two financed research projects for the Ministry of Higher Education Research Innovation in Oman via TRC. His research interests include the Internet of Things, artificial intelligence and machine learning, deep learning, cloud computing, and cyber security. He has published more than 130 technical articles in journals and conferences throughout the world.
Dr. A. Parvathy received her B.E. degree in Electronics and Communication Engineering from NIT, Trichy in 2002 and M.E. in Computer and Communication from Anna University, Chennai, India in 2006. She received her Ph.D in "Radio Frequency Identification (RFID) on AWS cloud to predict the student's performances using Machine Learning and Deep learning algorithms" at SASTRA Deemed to be University, Thanjavur, India in 2021. Her areas of interest include Embedded Systems, Internet of Things (IoT) and Machine Learning. She is currently working as Assistant Professor for the past 25 years and handled the value-added course like IoT Domain security SASTRA Deemed to be University, Tamil Nadu, India. She has delivered the lectures on "Recent trends and technologies on IoT" for The Institution of Engineers India (IEI) and "The National level Technical Symposium (Bio-FEAST) India" in 2024. She has published three IEEE Transaction papers (ML), 4 SCI (RFID) and 18 (RFID and IoT) in reputed international journals.
Dr. K. Kavitha is an Assistant Professor in the Department of Computer Science at Mother Teresa Women's University, Kodaikanal, Tamil Nadu. She holds a PhD in Computer Science from Madurai Kamaraj University (2014) and has over 17 years of teaching experience and 9 years of research experience. Her research interests include Data Mining, Data Analytics, Cloud Computing, and the Internet of Things. She has supervised multiple PhD and M.Phil. scholars and has published extensively in international and national journals and conferences. She is an active member of professional bodies such as IAENG. She has served on the Board of Studies for Computer Science at Madurai Kamaraj University and Mother Teresa Women's University. Kavitha has been key in organizing national conferences and securing research grants, including funding for DST-sponsored AI lab setups. She is also involved in institutional committees for placement, website updates, and distance education inspections. Her contributions to academia and research continue to impact the field of computer science.
Content
.- A Technical Perspective on Reinforcement Learning (RL) and RL Algorithms.
.- An Introduction to Multi-Agent Reinforcement Learning (MARL).
.- Demystifying Deep Reinforcement Learning (DRL).
.- Delineating Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF).
.- Expounding Reinforcement Fine-Tuning (RFT).
.- Illustrating Reinforcement Learning and Its Applications In NLP.
.- Delineating Deep Reinforcement Learning (DRL) Applications for the Transportation Industry.
.- Demystifying Automated Reinforcement Learning (AutoRL) and Federated Reinforcement Learning.
.- An Overview of Reinforcement Learning Route Optimization Model to Accomplish Heightened Energy Efficiency in the Internet of Vehicles (IoVs).
.- A Survey on Reinforcement Learning for Ridesharing.
.- How Reinforcement Learning Empowers Transport Logistics.
.- Leveraging Multi-agent Reinforcement Learning (MARL) capability towards structured advanced air mobility.
.- A Survey on Deep Reinforcement Learning for Intelligent Transportation Systems.
.- Deep Reinforcement Learning for Optimal Scheduling Requirements in the Transport Sector.
.- A multi-agent deep reinforcement learning Method for traffic signal coordination.
.- A reinforcement learning approach for reducing traffic congestion using deep Q learning.