Deep Reinforcement Learning and Its Industrial Use Cases

AI for Real-World Applications

Shubham Mahajan, Pethuru Raj, Amit Kant Pandit

• Publisher: Wiley-Scrivener
• Year: 2024
• Print ISBN: 9781394272556
• eBook ISBN: 9781394272570
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation.

Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques.

"Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications" is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments.

This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively.

Audience

The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Deep Reinforcement Learning Applications in Real-World Scenarios: Challenges and Opportunities
• Chapter 2 Deep Reinforcement Learning: A Key to Unlocking the Potential of Robotics and Autonomous Systems
• Chapter 3 Deep Reinforcement Learning Algorithms: A Comprehensive Overview
• Chapter 4 Deep Reinforcement Learning in Healthcare and Biomedical Applications
• Chapter 5 Application of Deep Reinforcement Learning in Adversarial Malware Detection
• Chapter 6 Artificial Intelligence in Blockchain and Smart Contracts for Disruptive Innovation
• Chapter 7 Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
• Chapter 8 Cultivating Expertise in Deep and Reinforcement Learning Principles
• Chapter 9 Deep Reinforcement Learning in Healthcare and Biomedical Research
• Chapter 10 Deep Reinforcement Learning in Robotics and Autonomous Systems
• Chapter 11 Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning
• Chapter 12 Early Brain Stroke Detection Based on Optimized Cuckoo Search Using LSTM-Gated Multi-Perceptron Neural Network
• Chapter 13 Hybrid Approaches: Combining Deep Reinforcement Learning with Other Techniques
• Chapter 14 Predictive Modeling of Rheumatoid Arthritis Symptoms: A High-Performance Approach Using HSFO-SVM and UNET-CNN
• Chapter 15 Using Reinforcement Learning in Unity Environments for Training AI Agent
• Chapter 16 Emerging Technologies in Healthcare Systems
• Index
• EULA