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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Basic DQN

To get started, we will implement the same DQN method as in Chapter 6, Deep Q-Networks, but leveraging the high-level libraries described in Chapter 7, Higher-Level RL Libraries. This will make our code much more compact, which is good, as non-relevant details won't distract us from the method's logic.

At the same time, the purpose of this book is not to teach you how to use the existing libraries, but rather how to develop intuition about RL methods and, if necessary, implement everything from scratch. From my perspective, this is a much more valuable skill, as libraries come and go, but true understanding of the domain will allow you to quickly make sense of other people's code and apply it consciously.

In the basic DQN implementation, we have three modules:

  • Chapter08/lib/ the DQN neural network (NN), which is the same as Chapter 6, so I won't repeat it
  • Chapter08/lib/ common functions and declarations shared by...