#### 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 an MDP

Developed upon the Markov chain, an MDP involves an agent and a decision-making process. Let's go ahead with developing an MDP and calculating the value function under the optimal policy.

Besides a set of possible states, S = {s0, s1, ... , sm}, an MDP is defined by a set of actions, A = {a0, a1, ... , an}; a transition model, T(s, a, s'); a reward function, R(s); and a discount factor, 𝝲. The transition matrix, T(s, a, s'), contains the probabilities of taking action a from state s then landing in s'. The discount factor, 𝝲, controls the tradeoff between future rewards and immediate ones.

To make our MDP slightly more complicated, we extend the study and sleep process with one more state, s2 play games. Let's say we have two actions, a0 work and a1 slack. The 3 * 2 * 3 transition matrix T(s, a, s') is as follows:

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