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 Cliff Walking with the actor-critic algorithm

In this recipe, let's solve a more complicated Cliff Walking environment using the A2C algorithm.

Cliff Walking is a typical Gym environment with long episodes without a guarantee of termination. It is a grid problem with a 4 * 12 board. An agent makes a move of up, right, down and left at a step. The bottom-left tile is the starting point for the agent, and the bottom-right is the winning point where an episode will end if it is reached. The remaining tiles in the last row are cliffs where the agent will be reset to the starting position after stepping on any of them, but the episode continues. Each step the agent takes incurs a -1 reward, with the exception of stepping on the cliffs, where a -100 reward incurs.

The state is an integer from 0 to 47, indicating where the agent is located, as illustrated:

Such value does...