Deep Reinforcement Learning and Its Industrial Use Cases

Shubham Mahajan , Pethuru Raj , Amit Kant Pandit 지음

Wiley-Scrivener 2024년 10월 01일 출간

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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

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Preface 1. Deep Reinforcement Learning Applications in Real-World Scenarios: Challenges and Opportunities Sunilkumar Ketineni and Sheela J. 2. Deep Reinforcement Learning: A Key to Unlocking the Potential of Robotics and Autonomous Systems Saksham and Chhavi Rana 3. Deep Reinforcement Learning Algorithms: A Comprehensive Overview Shweta V. Bondre, Bhakti Thakre, Uma Yadav and Vipin D. Bondre 4. Deep Reinforcement Learning in Healthcare and Biomedical Applications Balakrishnan D., Aarthy C., Nandhagopal Subramani, Venkatesan R. and Logesh T. R. 5. Application of Deep Reinforcement Learning in Adversarial Malware Detection Manju and Chhavi Rana 6. Artificial Intelligence in Blockchain and Smart Contracts for Disruptive Innovation Eashwar Sivakumar, Kiran Jot Singh and Paras Chawla 7. Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements Keerthika K., Kannan M. and T. Saravanan 8. Cultivating Expertise in Deep and Reinforcement Learning Principles Chilakalapudi Malathi and J. Sheela 9. . Deep Reinforcement Learning in Healthcare and Biomedical Research Shruti Agrawal and Pralay Mitra 10. Deep Reinforcement Learning in Robotics and Autonomous Systems Uma Yadav, Shweta V. Bondre and Bhakti Tha 11. Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning H.R. Manjunatha and P. Sathish 12. Early Brain Stroke Detection Based on Optimized Cuckoo Search Using LSTM‑Gated Multi-Perceptron Neural Network Anita Venaik, Asha A., Dhiyanesh B., Kiruthiga G., Shakkeera L. and Vinodkumar Jacob 13. Hybrid Approaches: Combining Deep Reinforcement Learning with Other Techniques M. T. Vasumathi, Manju Sadasivan and Aurangjeb Khan 14. Predictive Modeling of Rheumato

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저자(글) Shubham Mahajan

저자(글) Pethuru Raj

저자(글) Amit Kant Pandit