Algorithms for Reinforcement Learning is a comprehensive book that provides an in-depth overview of reinforcement learning (RL), a learning paradigm focused on learning to control a system to maximize a long-term objective. The book is written by Csaba Szepesvári and published by Springer. It is part of the Synthesis Lectures on Artificial Intelligence and Machine Learning series, edited by Ronald J. Brachman and Thomas Dietterich. The book covers a wide range of topics in RL, including Markov Decision Processes (MDPs), value prediction, control, and function approximation. It also discusses various algorithms, such as temporal difference learning, gradient temporal difference learning, least-squares methods, and actor-critic methods. The book is intended for advanced undergraduate and graduate students, as well as researchers and practitioners in the field of RL. It assumes familiarity with linear algebra, calculus, and probability theory, and provides a detailed explanation of the necessary concepts. The book is structured into three parts: the first part provides the necessary background on MDPs and dynamic programming, the second part focuses on value prediction problems, and the third part is devoted to control learning. The book also includes a list of topics for further exploration. The author thanks his family, colleagues, and students for their support and contributions to the book. The book is a valuable resource for anyone interested in understanding the principles and applications of reinforcement learning.Algorithms for Reinforcement Learning is a comprehensive book that provides an in-depth overview of reinforcement learning (RL), a learning paradigm focused on learning to control a system to maximize a long-term objective. The book is written by Csaba Szepesvári and published by Springer. It is part of the Synthesis Lectures on Artificial Intelligence and Machine Learning series, edited by Ronald J. Brachman and Thomas Dietterich. The book covers a wide range of topics in RL, including Markov Decision Processes (MDPs), value prediction, control, and function approximation. It also discusses various algorithms, such as temporal difference learning, gradient temporal difference learning, least-squares methods, and actor-critic methods. The book is intended for advanced undergraduate and graduate students, as well as researchers and practitioners in the field of RL. It assumes familiarity with linear algebra, calculus, and probability theory, and provides a detailed explanation of the necessary concepts. The book is structured into three parts: the first part provides the necessary background on MDPs and dynamic programming, the second part focuses on value prediction problems, and the third part is devoted to control learning. The book also includes a list of topics for further exploration. The author thanks his family, colleagues, and students for their support and contributions to the book. The book is a valuable resource for anyone interested in understanding the principles and applications of reinforcement learning.