Preface xv 1 Deep Reinforcement Learning Applications in Real-World Scenarios: Challenges and Opportunities 1 Sunilkumar Ketineni and Sheela J. 1.1 Introduction 1 1.1.1 Problems with Real-World Implementation 2 1.2 Application to the Real World 3 1.2.1 Security and Robustness 3 1.2.2 Generalization 5 1.2.2.1 Overcoming Challenges in DRL 9 1.3 Possibilities for Making a Difference in the Real World 11 1.3.1 Transfer Learning and Domain Adaptation 11 1.4 Meta-Learning 12 1.5 Deep Reinforcement Learning (DRL) 13 1.5.1 Hybrid Approaches 14 1.6 Online vs. Offline Reinforcement Learning 15 1.7 Human-in-the-Loop Systems 15 1.8 Benchmarking and Standardization 16 1.9 Collaborative Multi-Agent Systems 18 1.10 Transfer Learning and Domain Adaptation 19 1.11 Hierarchical and Multimodal Learning 21 1.12 Imitation Learning and Human Feedback 22 1.13 Inverse Reinforcement Learning 23 1.14 Sim-to-Real Transfer 24 1.15 Conclusion 25 References 26 2 Deep Reinforcement Learning: A Key to Unlocking the Potential of Robotics and Autonomous Systems 29 Saksham and Chhavi Rana 2.1 Introduction 30 2.1.1 Significance of DRL Field 30 2.1.2 Transformative Advantages of DRL Field 32 2.2 Fields of Investigation 33 2.2.1 General Methods for Investigation 34 2.3 Background 36 2.3.1 Fundamentals of Deep Reinforcement Learning (DRL) 38 2.4 Deep Reinforcement Learning (DRL) in Robot Control 39 2.4.1 Navigation and Localization 40 2.4.2 Object Manipulation 42 2.5 Applications and Case Studies 43 2.6 Challenges and Future Directions 44 2.7 Evaluation and Metrics 46 2.8 Summary 47 References 48 3 Deep Reinforcement Learning Algorithms: A Comprehensive Overview 51 Shweta V. Bondre, Bhakti Thakre, Uma Yadav and Vipin D. Bondre 3.1 Introduction 52 3.1.1 How Reinforcement Learning Works? 53 3.2 Reinforcement Learning Algorithms 53 3.2.1 Value-Based Algorithms 53 3.2.1.1 Q-Learning 53 3.2.1.2 Deep Q-Networks (DQN) 57 3.2.1.3 Double DQN 58 3.2.1.4 Dueling DQN 58 3.3 Policy-Based 59 3.3.1 Policy Gradient Methods 59 3.3.2 REINFORCE (Monte Carlo Policy Gradient) 60 3.3.3 Actor–Critic Methods 61 3.3.4 Natural Policy Gradient Methods 62 3.4 Model-Based Reinforcement Learning 63 3.4.1 Probabilistic Ensembles with Trajectory Sampling (PETS) 63 3.4.2 Probabilistic Inference for Learning Control (PILCO) 64 3.4.3 Model Predictive Control (MPC) 65 3.4.4 Model-Agnostic Meta-Learning (MAML) 66 3.4.5 Soft Actor–Critic with Model Ensemble 67 3.4.6 Deep Deterministic Policy Gradients with Model (DDPG with Model) 68 3.5 Characteristics of Reinforcement Learning 69 3.6 DRL Algorithms and Their Advantages and Drawbacks 71 3.7 Conclusion 72 References 72 4 Deep Reinforcement Learning in Healthcare and Biomedical Applications 75 Balakrishnan D., Aarthy C., Nandhagopal Subramani, Venkatesan R. and Logesh T. R. 4.1 Introduction 76 4.2 Related Works 76 4.3 Deep Reinforcement Learning Framework 80 4.4 Deep Reinforcement Learning Applications in Healthcare and Biomedicine 81 4.5 Deep Reinforcement Learning Employs Efficient Algorithms 82 4.5.1 Deep Q-Networks 82 4.5.2 Policy Differentiation Techniques 82 4.5.3 Hindsight Experience Replay (HER) 82 4.5.4 Curiosity-Driven Exploration 82 4.5.5 Long Short-Term Memory Networks and Recurring Neural Network Designs 82 4.5.6 Multi-Agent DRL 83 4.6 Semi-Autonomous Control Based on Deep Reinforcement Learning for Robotic Surgery 83 4.6.1 Double Deep Q-Network (DDQN) 83 4.6.2 Materials and Methods 84 4.6.3 Results 86 4.6.4 Discussion 87 4.7 Conclusion 87 References 88 5 Application of Deep Reinforcement Learning in Adversarial Malware Detection 91 Manju and Chhavi Rana 5.1 Introduction 91 5.1.1 Background 95 5.1.2 Significance of Malware Detection 96 5.1.3 Challenges with Adversarial Attacks 96 5.2 Foundations of Deep Reinforcement Learning 97 5.2.1 Overview of Deep Reinforcement Learning 98 5.2.2 Core Concepts and Components 99 5.2.3 Relevance to Malware Detection 100 5.3 Malware Detection Landscape 101 5.3.1 Evolution of Malware Detection Techniques 102 5.3.2 Adversarial Attacks in Cybersecurity 103 5.3.3 Need for Advanced Detection Strategies 104 5.4 Deep Reinforcement Learning Techniques 104 5.4.1 Application of Deep Learning in Malware Detection 105 5.4.2 Reinforcement Learning Algorithms 106 5.5 Feature Selection Strategies 107 5.5.1 Importance of Feature Selection in Malware Detection 108 5.5.2 Techniques for Feature Selection 108 5.5.3 Optimization for Deep Reinforcement Learning Models 109 5.6 Datasets and Evaluation 110 5.7 Generating Adversarial Samples 111 Conclusion and Future Directions 112 Future Directions 112 References 112 6 Artificial Intelligence in Blockchain and Smart Contracts for Disruptive Innovation 115 Eashwar Sivakumar, Kiran Jot Singh and Paras Chawla 6.1 Introduction 115 6.1.1 Smart Contract 116 6.2 Literature Review 117 6.2.1 Blockchain and Smart Contracts in Digital Identity 117 6.2.2 Blockchain and Smart Contracts in Financial Security 118 6.2.3 Blockchain and Smart Contracts in Supply Chain Management 119 6.2.4 Blockchain and Smart Contracts in Insurance 120 6.2.5 Blockchain and Smart Contracts in Healthcare 121 6.2.6 Blockchain and Smart Contracts in Agriculture 121 6.2.7 Blockchain and Smart Contracts in Real Estate 122 6.2.8 Blockchain and Smart Contracts in Education and Research 123 6.2.9 Blockchain and Smart Contracts in Other Sectors 124 6.3 Critical Analysis of the Review 125 6.4 Blockchain and Artificial Intelligence 128 6.5 Discussion on the Reasoning for Implementation of Blockchain 129 6.6 Conclusion 130 References 130 7 Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements 137 Keerthika K., Kannan M. and T. Saravanan 7.1 Introduction 138 7.2 Deep Reinforcement Learning Methods 138 7.2.1 Model-Free Methods 138 7.2.2 Policy Gradient Methods 139 7.2.3 Model-Based Methods 139 7.3 Applications of DRL in Healthcare 140 7.3.1 Tailored Treatment Recommendations 140 7.3.2 Optimization of Clinical Trials 141 7.3.3 Disease Diagnosis Support 142 7.3.4 Accelerated Drug Discovery and Design 142 7.3.5 Enhanced Robotic Surgery and Assistance 142 7.3.6 Health Management System 143 7.4 Challenges 143 7.5 Healthcare Data Types 144 7.5.1 Electronic Healthcare Records (EHRs) 144 7.5.2 Laboratory Data 145 7.5.3 Sensor Data 145 7.5.4 Biomedical Imaging Information 145 7.6 Guidelines for the Application of DRL 147 7.7 A Case Study: DRL in Healthcare and Biomedical Applications 147 7.7.1 Optimizing Radiation Therapy Dose Distribution in Cancer Treatment 147 7.7.2 Dose Strategy Model in Sepsis Patient Treatment 148 References 149 8 Cultivating Expertise in Deep and Reinforcement Learning Principles 151 Chilakalapudi Malathi and J. Sheela 8.1 Introduction 151 8.1.1 Reinforcement Learning’s Constituent Parts 152 8.1.2 Process of Markov Decisions (MDP) 152 8.1.3 Learning Reinforcement Methods 153 8.2 Intensive Learning Foundations 164 8.2.1 A Definition of Deep Learning 164 8.2.2 Deep Learning Elements 164 8.2.2.1 Different Kinds of Deep Learning Networks 165 8.3 Integrating Deep Learning and Reinforcement Learning 172 8.3.1 Deep Reinforcement Learning 172 8.3.2 Deep Reinforcement Learning Complexity Problems 174 Conclusion 175 References 175 9 Deep Reinforcement Learning in Healthcare and Biomedical Research 179 Shruti Agrawal and Pralay Mitra 9.1 Introduction 180 9.1.1 Reinforcement Learning 180 9.1.2 Deep Reinforcement Learning 181 9.2 Learning Methods in Bioinformatics with Applications in Healthcare and Biomedical Research 182 9.2.1 Protein Folding 182 9.2.2 Protein Docking 183 9.2.3 Protein–Ligand Binding 185 9.2.4 Binding Peptide Generation 187 9.2.5 Protein Design and Engineering 188 9.2.6 Drug Discovery and Development 190 9.3 Applications in Biological Data 192 9.3.1 Omics Data 192 9.3.2 Medical Imaging 192 9.3.3 Brain/Body–Machine Interfaces 193 9.4 Adaptive Treatment Approach in Healthcare 193 9.5 Diagnostic Tools in Healthcare and Biomedical Research 195 9.6 Scope of Deep Reinforcement Learning in Healthcare and Biomedical Applications 196 9.6.1 State and Action Space 196 9.6.2 Reward 197 9.6.3 Policy 198 9.6.4 Model Training 199 9.6.5 Exploration 199 9.6.6 Credit Assignment 200 9.7 Conclusions 200 References 201 10 Deep Reinforcement Learning in Robotics and Autonomous Systems 207 Uma Yadav, Shweta V. Bondre and Bhakti Thakre 10.1 Introduction 208 10.2 The Promise of Deep Reinforcement Learning (DRL) in Real-World Robotics 210 10.3 Preliminaries 211 10.4 Enhancing RL for Real-World Robotics 222 10.5 Reinforcement Learning for Various Robotic Applications 224 10.6 Problems Faced in RL for Robotics 231 10.7 RL in Robotics: Trends and Challenges 232 10.8 Conclusion 235 References 236 11 Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning 239 H.R. Manjunatha and P. Sathish 11.1 Introduction 239 11.2 Literature Survey 243 11.3 Diabetic Retinopathy Detection and Classification 248 11.4 Result Analysis 256 11.5 Conclusion 260 References 260 12 Early Brain Stroke Detection Based on Optimized Cuckoo Search Using LSTM‐Gated Multi-Perceptron Neural Network 265 Anita Venaik, Asha A., Dhiyanesh B., Kiruthiga G., Shakkeera L. and Vinodkumar Jacob 12.1 Introduction 266 12.2 Literature Survey 268 12.2.1 Problem Statement 269 12.3 Proposed Methodology 270 12.3.1 Dataset Collection 270 12.3.2 Preprocessing 271 12.3.3 Genetic Feature Sequence Algorithm (GFSA) 275 12.3.4 Disease-Prone Factor (DPF) 281 12.3.5 Decision Tree-Optimized Cuckoo Search (DTOCS) 284 12.3.6 Long Short-Term Memory Gate Multilayer Perceptron Neural Network (LSTM-MLPNN) 289 12.4 Result and Discussion 293 12.4.1 Performance Matrix 293 12.5 Conclusion 296 References 297 13 Hybrid Approaches: Combining Deep Reinforcement Learning with Other Techniques 301 M. T. Vasumathi, Manju Sadasivan and Aurangjeb Khan 13.1 Introduction 302 13.1.1 Digital Twin—Introduction 302 13.1.2 Model of a Digital Twin 302 13.1.2.1 Steps Involved in Building a Digital Twin Prototype 303 13.1.3 Application Areas of Digital Twins 303 13.1.3.1 Digital Twin in Medical Field 304 13.1.3.2 Digital Twin in Smart City 304 13.1.3.3 Digital Twin in Sports 304 13.1.3.4 Digital Twin in Smart Manufacturing 305 13.2 Digital Twin Technologies 305 13.2.1 Data Acquisition and Sensors 306 13.2.2 Data Analytics and Machine Learning 306 13.2.3 Cloud Computing 307 13.2.4 Other Technologies 307 13.3 Integration of RL and Digital Twin 307 13.3.1 Motivation for Combining Digital Twin and RL 309 13.3.2 How RL Enhances Decision-Making Within Digital Twins 310 13.4 Challenges of Using RL in Digital Twins 311 13.5 Digital Twin Modeling with RL 312 13.6 Technology Underlying RL-Based Digital Twins 314 13.6.1 Integration of RL with Digital Twins in Four Stages 314 13.6.2 Tools and Libraries for Developing RL-Based Digital Twins 314 13.6.2.1 Simulation and Digital Twin Platforms 314 13.6.2.2 Reinforcement Learning Libraries 315 13.6.3 Integration with Existing Systems and IoT Devices for RL Deployment 315 13.6.3.1 Data Collection and Sensor Integration 315 13.6.3.2 Communication and Data Ingestion 316 13.6.3.3 Digital Twin Integration 316 13.6.3.4 RL Integration 316 13.6.3.5 Control and Actuation 316 13.6.3.6 Implementation of Feedback and Learning Process 316 13.6.3.7 Dashboard for Alert and Visualization 316 13.6.3.8 Ensuring the Security and Authentication 317 13.7 Industry-Specific Applications: A Case Study of DT in a Car Manufacturing Unit 317 13.7.1 IoT Components Required for Creating Digital Twin for the Manufacturing Unit 318 13.7.2 Architecture of the Proposed Digital Twin for Car Manufacturing Unit 318 13.7.3 Challenges and Opportunities in the Implementation of DTs for Car Manufacturing 320 13.8 Conclusion 321 References 322 14 Predictive Modeling of Rheumatoid Arthritis Symptoms: A High-Performance Approach Using HSFO-SVM and UNET-CNN 325 Anusuya V., Baseera A., Dhiyanesh B., Parveen Begam Abdul Kareem and Shanmugaraja P. 14.1 Introduction 326 14.1.1 Novelty of the Research 327 14.2 Related Work 328 14.2.1 Challenges and Problem Identification Factor 331 14.3 HSFO-SVM Based on LSTM-Gated Convolution Neural Network (lstmg-cnn) 332 14.3.1 C-Score and Cross-Fold Validation 332 14.3.2 Honey Scout Forager Optimization 335 14.3.3 Feature Selection Using SVM 336 14.3.4 UNET-CNN Classification 338 14.4 Result and Discussion 341 14.5 Conclusion 345 References 346 15 Using Reinforcement Learning in Unity Environments for Training AI Agent 349 Geetika Munjal and Monika Lamba 15.1 Introduction 349 15.2 Literature Review 351 15.3 Machine Learning 352 15.3.1 Categorization of Machine Learning 352 15.3.1.1 Supervised Learning 352 15.3.1.2 Unsupervised Learning 353 15.3.1.3 Reinforcement Learning 353 15.3.2 Classifying on the Basis of Envisioned Output 353 15.3.2.1 Classification 354 15.3.2.2 Regression 354 15.3.2.3 Clustering 354 15.3.3 Artificial Intelligence 354 15.4 Unity 354 15.4.1 Unity Hub 355 15.4.2 Unity Editor 355 15.4.3 Inspector 355 15.4.4 Game View 355 15.4.5 Scene View 355 15.4.6 Hierarchy 355 15.4.7 Project Window 356 15.5 Reinforcement Learning and Supervised Learning 356 15.5.1 Positive Reinforcement 357 15.5.2 Negative Reinforcement 357 15.5.3 Model-Free and Model-Based RL 357 15.6 Proposed Model 359 15.6.1 Setting Up a Virtual Environment 359 15.6.2 Setting Up of the Environment 360 15.6.2.1 Creating and Allocating Scripts for the Environment 361 15.6.2.2 Creating a Goal for the Agent 361 15.6.2.3 Reward-Driven Behavior 361 15.7 Markov Decision Process 362 15.8 Model-Based RL 362 15.9 Experimental Results 363 15.9.1 Machine Learning Models Used for the Environments 363 15.9.2 PushBlock 363 15.9.3 Hallway 365 15.9.4 Screenshots of the PushBlock Environment 368 15.9.5 Screenshots of the Hallway Environment 369 15.10 Conclusion 372 References 372 16 Emerging Technologies in Healthcare Systems 375 Ravi Kumar Sachdeva, Priyanka Bathla, Samriti Vij, Dishika, Madhur Jain, Lokesh Kumar, G. S. Pradeep Ghantasala and Rakesh Ahuja 16.1 Introduction 375 16.2 Personalized Medicine 376 16.3 AI and ML in Healthcare Sector 377 16.3.1 AI in Medical Diagnosis 378 16.3.2 Drug Discovery 378 16.3.3 Personalized Treatment Plans 379 16.3.4 Pattern Matching or Trend Detection 380 16.4 Immunotherapy 380 16.4.1 Monoclonal Antibodies 381 16.4.2 Checkpoint Inhibitors 381 16.4.3 CAR-T Cell Therapy 381 16.5 Regenerative Medicine 381 16.6 Digital Health (Use of Technology in Healthcare) 383 16.6.1 Wearable Devices 383 16.6.2 Telemedicine 384 16.6.3 Electronic Health Records 384 16.7 Health Inequity 385 16.7.1 Health Disparity 385 16.7.2 Health Equity 385 16.8 Future Directions in Healthcare Research 385 16.9 Challenges and Recommendations for Advanced Level of Modern Healthcare Technologies 386 16.9.1 Challenges 387 16.9.2 Recommendations 388 16.10 Healthcare Sector in Developing and Underdeveloped Countries 388 16.10.1 Healthcare Sector in Developing Countries 388 16.10.2 Healthcare Sector in Underdeveloped Countries 389 16.11 Comparison of Recent Progress and Future Mentoring in Healthcare Using Technology 389 16.12 Conclusion 391 References 392 Index 395

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.