Deep Reinforcement Learning and Its Industrial Use Cases

Shubham Mahajan, PhD, is an assistant professor in the School of Engineering at Ajeekya D Y Patil University, Pune, Maharashtra, India. He has eight Indian, one Australian, and one German patent to his credit in artificial intelligence and image processing. He has authored/co-authored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication. Pethuru Raj, PhD, is chief architect and vice president at Reliance Jio Platforms Ltd in Bangalore, India. He has a PhD in computer science and automation from the Indian Institute of Science in Bangalore, India. His areas of interest focus on artificial intelligence, model optimization, and reliability engineering. He has published thirty research papers and edited forty-two books. Amit Kant Pandit, PhD, is an associate professor in the School of Electronics & Communication Engineering Shri Mata Vaishno Devi University, India. He has authored/co-authored more than 60 publications including peer-reviewed journals and conferences. He has two Indian and one Australian patent to his credit in artificial intelligence and image processing. His main research interests are image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization.

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 Rheumatoid Arthritis Symptoms: A High-Performance Approach Using HSFO-SVM and UNET-CNN
Anusuya V., Baseera A., Dhiyanesh B., Parveen Begam Abdul Kareem and Shanmugaraja P.

15. Using Reinforcement Learning in Unity Environments for Training AI Agent
Geetika Munjal and Monika Lamba

16. Emerging Technologies in Healthcare Systems
Ravi Kumar Sachdeva, Priyanka Bathla, Samriti Vij, Dishika, Madhur Jain, Lokesh Kumar, G. S. Pradeep Ghantasala and Rakesh Ahuja

References

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