Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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)
26
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27
Index

Deep Q-Networks

In Chapter 5, Tabular Learning and the Bellman Equation, you became familiar with the Bellman equation and the practical method of its application called value iteration. This approach allowed us to significantly improve our speed and convergence in the FrozenLake environment, which is promising, but can we go further? In this chapter, we will apply the same approach to problems of much greater complexity: arcade games from the Atari 2600 platform, which are the de facto benchmark of the reinforcement learning (RL) research community.

To deal with this new and more challenging goal, in this chapter, we will:

  • Talk about problems with the value iteration method and consider its variation, called Q-learning.
  • Apply Q-learning to so-called grid world environments, which is called tabular Q-learning.
  • Discuss Q-learning in conjunction with neural networks (NNs). This combination has the name deep Q-network (DQN).

At the end of the chapter, we will...