#### Overview of this book

Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.
Preface
Free Chapter
Getting Started with Reinforcement Learning and PyTorch
Markov Decision Processes and Dynamic Programming
Monte Carlo Methods for Making Numerical Estimations
Capstone Project – Playing Flappy Bird with DQN
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# Playing Blackjack with Monte Carlo prediction

In this recipe, we will play Blackjack (also called 21) and evaluate a policy we think might work well. You will get more familiar with Monte Carlo prediction with the Blackjack example, and get ready to search for the optimal policy using Monte Carlo control in the upcoming recipes.

Blackjack is a popular card game where the goal is to have the sum of cards as close to 21 as possible without exceeding it. The J, K, and Q cards have a points value of 10, and cards from 2 to 10 have values from 2 to 10. The ace card can be either 1 or 11 points; when the latter value is chosen, it is called a usable ace. The player competes against a dealer. At the beginning, both parties are given two random cards, but only one of the dealer's cards is revealed to the player. The player can request additional cards (called hit) or stop receiving...