Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Index

Chapter 11. Asynchronous Advantage Actor-Critic

This chapter is dedicated to the extension of the Actor-Critic (A2C) method that we discussed in detail in the previous chapter. The extension adds true asynchronous environment interaction. The full name is Asynchronous Advantage Actor-Critic, which is normally abbreviated to A3C. This method is one of the most widely used by RL practitioners. We will take a look at two approaches for adding asynchronous behavior to the basic A2C method.