The Book of Reinforcement Learning

The Book of Reinforcement Learning is a comprehensive guide that explains how computers can learn to make decisions by trying actions and learning from the results. It mirrors how humans and animals learn through trial and error, receiving feedback (rewards or penalties) to improve future choices. The book covers key concepts like agents, environments, rewards, and strategies for optimizing behavior over time. It aims to make complex ideas accessible, helping readers understand how machines can autonomously develop skills in areas like game playing, robotics, and personalized recommendations through continuous learning and adaptation.