#### 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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# Estimating Q-functions with gradient descent approximation

Starting from this recipe, we will develop FA algorithms to solve environments with continuous state variables. We will begin by approximating Q-functions using linear functions and gradient descent.

The main idea of FA is to use a set of features to estimate Q values. This is extremely useful for processes with a large state space where the Q table becomes huge. There are several ways to map the features to the Q values; for example, linear approximations that are linear combinations of features and neural networks. With linear approximation, the state-value function for an action is expressed by a weighted sum of the features:

Here, F1(s), F2(s), ……, Fn(s) is a set of features given the input state, s; θ1, θ2,......, θn are the weights applied to corresponding features. Or we can put...