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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Other RL libraries

As we discussed earlier, there are several RL-specific libraries available. Overall, TensorFlow is more popular than PyTorch, as it is more widespread in the deep learning community. The following is my (very biased) list of libraries:

  • Keras-RL: started by Matthias Plappert in 2016, this includes basic deep RL methods. As suggested by the name, this library was implemented using Keras, which is a higher-level wrapper around TensorFlow (
  • Dopamine: a library from Google published in 2018. It is TensorFlow-specific, which is not surprising for a library from Google (
  • Ray: a library for distributed execution of machine learning code. It includes RL utilities as part of the library (
  • TF-Agents: another library from Google published in 2018 (
  • ReAgent: a library from Facebook Research. It uses PyTorch...