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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Higher-Level RL Libraries

In Chapter 6, Deep Q-Networks, we implemented the deep Q-network (DQN) model published by DeepMind in 2015 ( This paper had a significant effect on the RL field by demonstrating that, despite common belief, it's possible to use nonlinear approximators in RL. This proof of concept stimulated great interest in the deep Q-learning field and in deep RL in general.

In this chapter, we will take another step towards practical RL by discussing higher-level RL libraries, which will allow you to build your code from higher-level blocks and focus on the details of the method that you are implementing. Most of the chapter will describe the PyTorch Agent Net (PTAN) library, which will be used in the rest of the book to avoid code repetition, so will be covered in detail.

We will cover:

  • The motivation for using high-level libraries, rather than reimplementing everything...