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)

Markov Decision Processes and Dynamic Programming

In this chapter, we will continue our practical reinforcement learning journey with PyTorch by looking at Markov decision processes (MDPs) and dynamic programming. This chapter will start with the creation of a Markov chain and an MDP, which is the core of most reinforcement learning algorithms. You will also become more familiar with Bellman equations by practicing policy evaluation. We will then move on and apply two approaches to solving an MDP: value iteration and policy iteration. We will use the FrozenLake environment as an example. At the end of the chapter, we will demonstrate how to solve the interesting coin-flipping gamble problem with dynamic programming step by step.

The following recipes will be covered in this chapter:

  • Creating a Markov chain
  • Creating an MDP
  • Performing policy evaluation
  • Simulating the FrozenLake...