#### 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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# Solving multi-armed bandit problems with the Thompson sampling algorithm

In this recipe, we will tackle the exploitation and exploration dilemma in the advertising bandits problem using another algorithm, Thompson sampling. We will see how it differs greatly from the previous three algorithms.

Thompson sampling (TS) is also called Bayesian bandits as it applies the Bayesian way of thinking from the following perspectives:

• It is a probabilistic algorithm.
• It computes the prior distribution for each arm and samples a value from each distribution.
• It then selects the arm with the highest value and observes the reward.
• Finally, it updates the prior distribution based on the observed reward. This process is called Bayesian updating.

As we have seen that in our ad optimization case, the reward for each arm is either 1 or 0. We can use beta distribution for our prior distribution because...