#### 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 an MDP with a policy iteration algorithm

Another approach to solving an MDP is by using a policy iteration algorithm, which we will discuss in this recipe.

A policy iteration algorithm can be subdivided into two components: policy evaluation and policy improvement. It starts with an arbitrary policy. And in each iteration, it first computes the policy values given the latest policy, based on the Bellman expectation equation; it then extracts an improved policy out of the resulting policy values, based on the Bellman optimality equation. It iteratively evaluates the policy and generates an improved version until the policy doesn't change any more.

Let's develop a policy iteration algorithm and use it to solve the FrozenLake environment. After that, we will explain how it works.

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