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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Continuous Action Space

This chapter kicks off the advanced reinforcement learning (RL) part of the book by taking a look at a problem that has only been briefly mentioned: working with environments when our action space is not discrete. In this chapter, you will become familiar with the challenges that arise in such cases and learn how to solve them.

Continuous action space problems are an important subfield of RL, both theoretically and practically, because they have essential applications in robotics (which will be the subject of the next chapter), control problems, and other fields in which we communicate with physical objects.

In this chapter, we will:

  • Cover the continuous action space, why it is important, how it differs from the already familiar discrete action space, and the way it is implemented in the Gym API
  • Discuss the domain of continuous control using RL methods
  • Check three different algorithms on the problem of a four-legged robot