Reinforcement Learning for the Transportation Industry
A Guide to Implementing RL in Real-world Transportation Scenarios
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Beschreibung
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.
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.
Bridges RL theory with transportation-focused real-world deployment strategies Covers emerging RLHF, RLAIF, and LLM-guided RL for next-generation AI systems Includes practical case studies on smart mobility, air mobility, and sustainable transport
Autor*in
Pethuru Raj Chelliah
Themen in »Reinforcement Learning for the Transportation Industry«
Reinforcement Learning Deep Reinforcement Learning Multi-Agent Reinforcement Learning Transportation Industry Intelligent Transportation Systems Autonomous Vehicles Traffic Signal Control Traffic Congestion Reduction Electric Vehicle Routing Internet of Vehicles (IoV) Transport Logistics Ridesharing Optimization Advanced Air Mobility Reinforcement Learning from Human Feedback (RLHF) Reinforcement Learning from AI Feedback (RLAIF)