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Reinforcement learning books focus on how agents learn to make sequential decisions by interacting with an environment and maximizing cumulative rewards. This category spans foundational concepts like Markov decision processes, policies, value functions, and the exploration–exploitation trade-off, as well as core algorithms such as Q-learning, SARSA, policy gradients, actor–critic methods, and deep reinforcement learning. Titles range from introductory overviews to mathematically rigorous texts, often covering probability, dynamic programming, and function approximation with neural networks. Many books include practical guidance on implementation, simulation environments, and evaluation, alongside case studies and applications in robotics, game playing, recommendation systems, operations research, finance, and healthcare. Intended readers include students, researchers, and practitioners in machine learning, AI, and data-driven decision-making.

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