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 the REINFORCE algorithm

A recent publication stipulated that policy gradient methods are becoming more and more popular. Their learning goal is to optimize the probability distribution of actions so that given a state, a more rewarding action will have a higher probability value. In the first recipe of the chapter, we will talk about the REINFORCE algorithm, which is foundational to advanced policy gradient methods.

The REINFORCE algorithm is also known as the Monte Carlo policy gradient, as it optimizes the policy based on Monte Carlo methods. Specifically, it collects trajectory samples from one episode using its current policy and uses them to the policy parameters, θ . The learning objective function for policy gradients is as follows:

Its gradient can be derived as follows:

Here, is the return, which is the cumulative discounted reward until time, t...