An Introduction to Reinforcement Learning

An Introduction to Reinforcement Learning invites readers into the dynamic world of intelligent agents that learn through trial and error. This book explores 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...