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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The first training objective

Let's now discuss what we want our robot to do and how we're going to get there. It's not very hard to notice that the potential capabilities of the hardware described are quite limited:

  • We have only four servos with a constrained angle of rotation: This makes our robot's movements highly dependent on friction with the surface, as it can't bring its individual legs up, which is also the case with the Minitaur robot, which has two motors attached to every leg.
  • Our hardware capacity is small: The memory is limited, the central processing unit (CPU) is not very fast, and no hardware accelerators are present. In the subsequent sections, we will take a look at how to deal with those limitations to some extent.
  • We have no external connectivity besides a micro-USB port: Some boards might have Wi-Fi hardware, which could be used to offload the NN inference to a larger machine, but in this chapter's example, I'm...