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Artificial Intelligence has made remarkable progress in recent years, with deep reinforcement learning enabling machines to solve complex decision-making problems across domains such as robotics, healthcare, finance, and autonomous systems. However, as these models become increasingly powerful, they also become more difficult to interpret, often functioning as opaque “black-box” systems.
Illuminating Intelligence: Explainable AI and Interpretability in Deep Reinforcement Learning explores the emerging field of Explainable Artificial Intelligence (XAI) and its role in making advanced AI systems more transparent and understandable. The book introduces the foundations of artificial intelligence and reinforcement learning, and examines key techniques for interpreting complex machine learning models.
Through discussions on model-agnostic explanations, visualization methods, feature attribution, and policy interpretability, the book provides practical insights into analysing and understanding deep reinforcement learning systems.
Designed for students, researchers, and practitioners in artificial intelligence and machine learning, this book offers a clear introduction to the challenges, techniques, and future directions of building transparent, trustworthy, and responsible AI systems.
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Akanksh Reddy Muddam, Lavanya Reddy Satti
Akanksh Reddy Muddam is a Machine Learning Engineer and Artificial Intelligence specialist with hands-on experience designing, training, and deploying machine learning systems for real-world applications. He holds a Master’s degree in Artificial Intelligence from the National College of Ireland and a Bachelor’s degree in Computer Science and Engineering. His work focuses on machine learning, deep learning, and AI-driven decision systems, including time-series forecasting and computer vision applications. His research interests include explainable artificial intelligence, deep reinforcement learning, and responsible AI systems.
Lavanya Reddy Satti is an academician with over 25 years of teaching experience across degree and engineering colleges. She is currently pursuing her PhD from SR University and serves as an Assistant Professor at Keshav Memorial Institute of Engineering and Technology. Her academic interests include artificial intelligence, emerging technologies, and advancing research-oriented education in engineering and computer science. Through her extensive teaching and research experience, she has contributed to nurturing students and promoting innovation in technology and engineering education.
Together, the authors combine practical industry experience with academic insight to present a comprehensive perspective on explainable artificial intelligence and deep reinforcement learning. Their work aims to bridge the gap between advanced AI research and real-world understanding, helping readers explore the importance of transparency, interpretability, and responsible development of modern AI systems.
