More Information
Learn
  • Use Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problems
  • Develop a multi-armed bandit algorithm to optimize display advertising
  • Scale up learning and control processes using Deep Q-Networks
  • Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems
  • Select and build RL models, evaluate their performance, and optimize and deploy them
  • Use policy gradient methods to solve continuous RL problems
About

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.

Features
  • Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models
  • Implement RL algorithms to solve control and optimization challenges faced by data scientists today
  • Apply modern RL libraries to simulate a controlled environment for your projects
Page Count 340
Course Length 10 hours 12 minutes
ISBN 9781838551964
Date Of Publication 31 Oct 2019

Authors

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his expertise in reinforcement learning to computational. He is an education enthusiast and the author of a series of ML books. His first book, Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.