#### 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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# Creating a Markov chain

Let's get started by creating a Markov chain, on which the MDP is developed.

A Markov chain describes a sequence of events that comply with the Markov property. It is defined by a set of possible states, S = {s0, s1, ... , sm}, and a transition matrix, T(s, s'), consisting of the probabilities of state s transitioning to state s'. With the Markov property, the future state of the process, given the present state, is conditionally independent of past states. In other words, the state of the process at t+1 is dependent only on the state at t. Here, we use a process of study and sleep as an example and create a Markov chain based on two states, s0 (study) and s1 (sleep). Let's say we have the following transition matrix:

In the next section, we will compute the transition matrix after k steps, and the probabilities of being in each state...