Brushing Up on Reinforcement Learning Concepts

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 mastering it will give you a solid foundation in your knowledge and practice of RL. Whether you work as a data scientist, machine learning engineer, or other practitioner in the data or AI space, you will find plenty of useful and practical resources to get you started. 

We will cover the following topics in this introductory chapter:

• Reviewing RL and the differences between reward-based learning and other types of machine learning 
• Learning what states are and what it means to take an action and receive a reward
• Understanding how RL agents make decisions based on policies and future rewards
• Discovering the two major types of model-free RL and diving deeper into Q-learning