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)

Getting Started with Reinforcement Learning and PyTorch

We kick off our journey of practical reinforcement learning and PyTorch with the basic, yet important, reinforcement learning algorithms, including random search, hill climbing, and policy gradient. We will start by setting up the working environment and OpenAI Gym, and you will become familiar with reinforcement learning environments through the Atari and CartPole playgrounds. We will also demonstrate how to develop algorithms to solve the CartPole problem step by step. Also, we will review the essentials of PyTorch and prepare for the upcoming learning examples and projects.

This chapter contains the following recipes:

  • Setting up the working environment
  • Installing OpenAI Gym
  • Simulating Atari environments
  • Simulating the CartPole environment
  • Reviewing the fundamentals of PyTorch
  • Implementing and evaluating a random search policy
  • Developing the hill-climbing algorithm
  • Developing a policy gradient algorithm