In this book, you will learn the fundamentals of Q-learning, a branch of reinforcement learning (RL), and how to apply them to challenging real-world optimization problems. You'll design software that dynamically writes itself, modifies itself, and improves its own performance in real time.
In doing so, you will build self-learning intelligent agents that start with no knowledge of how to solve a problem and independently find optimal solutions to that problem through observation, trial and error, and memory.
RL is one of the most exciting branches of artificial intelligence (AI) and powers some of its most visible successes, from recommendation systems that learn from user behavior to game-playing machines that can beat any human being at chess or Go.
Q-learning is one of the easiest versions of RL to get started with, and...
