What this book covers
Chapter 1, Brushing Up on Reinforcement Learning Concepts, covers the building-block concepts of agents, environments, states, actions, rewards, policies, and values. The reader will be introduced to stochastic and deterministic environments, learning rates, and exploration versus exploitation.
Chapter 2, Getting Started with the Q-Learning Algorithm, covers in great depth how a Markov decision process works and the way a Q-learning algorithm is designed to solve it. We will discuss what kinds of problems can and can't be solved with a model-free algorithm, as well as the types of problems that Q-learning is especially well suited to.
Chapter 3, Setting Up Your First Environment with OpenAI Gym, covers using OpenAI ...
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