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)

Implementing Policy Gradients and Policy Optimization

In this chapter, we will focus on policy gradient methods as one of the most popular reinforcement learning techniques over recent years. We will start with implementing the fundamental REINFORCE algorithm and will proceed with an improvement algorithm baseline. We will also implement a more powerful algorithm, actor-critic, and its variations, and apply it to solve the CartPole and Cliff Walking problems. We will also experience an environment with continuous action space and resort to Gaussian distribution to solve it. By way of a fun section at the end, we will train an agent based on the cross-entropy method to play the CartPole game.

The following recipes will be covered in this chapter:

  • Implementing the REINFORCE algorithm
  • Developing the REINFORCE algorithm with baseline
  • Implementing the actor-critic algorithm
  • Solving...