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)

Simulating the FrozenLake environment

The optimal policies for the MDPs we have dealt with so far are pretty intuitive. However, it won't be that straightforward in most cases, such as the FrozenLake environment. In this recipe, let's play around with the FrozenLake environment and get ready for upcoming recipes where we will find its optimal policy.

FrozenLake is a typical Gym environment with a discrete state space. It is about moving an agent from the starting location to the goal location in a grid world, and at the same time avoiding traps. The grid is either four by four ( or eight by eigh.

t ( The grid is made up of the following four types of tiles:

  • S: The starting location
  • G: The goal location, which terminates an episode
  • F: The frozen tile, which is a walkable location...