An Introduction to Reinforcement Learning invites readers into the dynamic world of intelligent agents that learn through trial and error. This book demystifies the core principles of reinforcement learning from the fundamental ideas of Markov decision processes and dynamic programming to cutting-edge techniques in deep RL policy gradients and multi-agent systems. Written in clear and accessible language it blends theoretical insights with practical examples and real-life analogies to show how m
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