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)

Building a Deep Q-Network to play Flappy Bird

Now that the Flappy Bird environment is ready, we can start tackling it by building a DQN model.

As we have seen, a screen image is returned at each step after an action is taken. A CNN is one of the best neural network architectures to deal with image inputs. In a CNN, the convolutional layers are able to effectively extract features from images, which will be passed on to fully connected layers downstream. In our solution, we will use a CNN with three convolutional layers and one fully connected hidden layer. An example of CNN architecture is as follows:

How to do it...

Let's develop a CNN-based DQN model as follows:

  1. Import the necessary modules:
>>> import...