Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

5 (2)
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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Model-based methods

To begin, let's discuss the difference between the model-free approach that we have used in the book and model-based methods, including their strong and weak points and where they might be applicable.

Model-based versus model-free

In the The taxonomy of RL methods section in Chapter 4, The Cross-Entropy Method, we saw several different angles from which we can classify RL methods. We distinguished three main aspects:

  • Value-based and policy-based
  • On-policy and off-policy
  • Model-free and model-based

There were enough examples of methods on both sides of the first and second categories, but all the methods that we have covered so far were 100% model-free. However, this doesn't mean that model-free methods are more important or better than their model-based counterparts. Historically, due to their sample efficiency, the model-based methods have been used in the robotics field and other industrial controls. This has also happened...