Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (1)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (1)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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Index

Asynchronous Advantage Actor-Critic

This chapter is dedicated to the extension of the advantage actor-critic (A2C) method that we discussed in detail in Chapter 12, The Actor-Critic Method. The extension adds true asynchronous environment interaction, and its full name is asynchronous advantage actor-critic, which is normally abbreviated to A3C. This method is one of the most widely used by reinforcement learning (RL) practitioners.

We will take a look at two approaches for adding asynchronous behavior to the basic A2C method: data-level and gradient-level parallelism. They have different resource requirements and characteristics, which makes them applicable to different situations.

In this chapter, we will:

  • Discuss why it is important for policy gradient methods to gather training data from multiple environments
  • Implement two different approaches to A3C