Book Image

PyTorch 1.x Reinforcement Learning Cookbook

By : Yuxi (Hayden) Liu
Book Image

PyTorch 1.x Reinforcement Learning Cookbook

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)

Solving Multi-armed Bandit Problems

Multi-armed bandit algorithms are probably among the most popular algorithms in reinforcement learning. This chapter will start by creating a multi-armed bandit and experimenting with random policies. We will focus on how to solve the multi-armed bandit problem using four strategies, including epsilon-greedy, softmax exploration, upper confidence bound, and Thompson sampling. We will see how they deal with the exploration-exploitation dilemma in their own unique ways. We will also work on a billion-dollar problem, online advertising, and demonstrate how to solve it using a multi-armed bandit algorithm. Finally, we will solve the contextual advertising problem using contextual bandits to make more informed decisions in ad optimization.

The following recipes will be covered in this chapter:

  • Creating a multi-armed bandit environment
  • Solving multi...