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PyTorch 1.x Reinforcement Learning Cookbook

PyTorch 1.x Reinforcement Learning Cookbook

By : Yuxi (Hayden) Liu
4.3 (3)
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PyTorch 1.x Reinforcement Learning Cookbook

PyTorch 1.x Reinforcement Learning Cookbook

4.3 (3)
By: Yuxi (Hayden) Liu

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.
Table of Contents (11 chapters)
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Preface

The surge in interest in reinforcement learning is due to the fact that it revolutionizes automation by learning the optimal actions to take in an environment in order to maximize the notion of cumulative reward.

PyTorch 1.x Reinforcement Learning Cookbook introduces you to important reinforcement learning concepts and implementations of algorithms in PyTorch. Each chapter of the book walks you through a different type of reinforcement learning method and its industry-adopted applications. With the help of recipes that contain real-world examples, you will find it intriguing to enhance your knowledge and proficiency of reinforcement learning techniques in areas such as dynamic programming, Monte Carlo methods, temporal difference and Q-learning, multi-armed bandit, function approximation, deep Q-Networks, and policy gradients—they are no more obscure than you thought. Interesting and easy-to-follow examples, such as Atari games, Blackjack, Gridworld environments, internet advertising, Mountain Car, and Flappy Bird, will keep you interested until you reach your goal.

By the end of this book, you will have mastered the implementation of popular reinforcement learning algorithms and learned the best practices of applying reinforcement learning techniques to solve other real-world problems.

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