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)

Deep Q-Networks in Action

Deep Q-learning, or using deep Q-networks, is considered the most modern reinforcement learning technique. In this chapter, we will develop various deep Q-network models step by step and apply them to solve several reinforcement learning problems. We will start with vanilla Q-networks and enhance them with experience replay. We will improve robustness by using an additional target network and demonstrate how to fine-tune a Deep Q-Network. We will also experiment with dueling deep Q-networks and see how their value functions differs from other types of Deep Q-Networks. In the last two recipes, we will solve complex Atari game problems by incorporating convolutional neural networks into Deep Q-Networks.

The following recipes will be covered in this chapter:

  • Developing deep Q-networks
  • Improving DQNs with experience replay
  • Developing double deep Q-Networks...