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)

Capstone Project – Playing Flappy Bird with DQN

In this very last chapter, we will work on a capstone project—playing Flappy Bird using reinforcement learning. We will apply what we have learned throughout this book to build an intelligent bot. We will also focus on building Deep Q-Networks (DQNs), fine-tuning model parameters, and deploying the model. Let's see how long the bird can stay in the air.

The capstone project will be built section by section in the following recipes:

  • Setting up the game environment
  • Building a Deep Q-Network to play Flappy Bird
  • Training and tuning the network
  • Deploying the model and playing the game

As a result, the code in each recipe is to be built on top of the previous recipes.