Reinforcement Learning Algorithms Analysis And Applications
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Reinforcement Learning Algorithms: Analysis and Applications
This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
Reinforcement Learning
Reinforcement Learning: A Practical Guide to Algorithms delves into the impactful world of reinforcement learning, a key branch of AI. Spanning over five decades, reinforcement learning has significantly advanced AI, offering solutions for planning, budgeting, and strategic decision-making. This book provides a comprehensive understanding of reinforcement learning, focusing on building smart models and agents that adapt to changing requirements. We cover fundamental and advanced topics, including value-based methods like UCB, SARSA, and Q-learning, as well as function approximation techniques. Additionally, we explore artificial neural networks, LSTD, gradient methods, emphatic TD methods, average reward methods, and policy gradient methods. With clear explanations, diagrams, and examples, this book ensures that readers can grasp and apply reinforcement learning algorithms to real-world problems effectively. By the end, you will have a solid foundation in both theoretical and practical aspects of reinforcement learning.
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.