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)

Solving the CartPole problem with function approximation

This is a bonus recipe in this chapter, where we will solve the CartPole problem using FA.

As we saw in Chapter 1, Getting started with reinforcement learning and PyTorch, we simulated the CartPole environment in the , Simulating the CartPole environment recipe, and solved the environment using random search, and the hill climbing and policy gradient algorithms, respectively, in recipes including Implementing and evaluating the random search policy, Developing the hill climbing algorithm, and Developing the policy gradient algorithm. Now, let's try to solve CartPole using what we've talked about in this chapter.

How to do it...

We demonstrate the solution...