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 PTAN library

The library is available in GitHub: All the subsequent examples were implemented using version 0.6 of PTAN, which can be installed in your virtual environment by running the following:

pip install ptan==0.6

The original goal of PTAN was to simplify my RL experiments, and it tries to keep the balance between two extremes:

  • Import the library and then write one line with tons of parameters to train one of the provided methods, like DQN (a very vivid example is the OpenAI Baselines project)
  • Implement everything from scratch

The first approach is very inflexible. It works well when you are using the library the way it is supposed to be used. But if you want to do something fancy, you will quickly find yourself hacking the library and fighting with the constraints that I imposed, rather than solving the problem you want to solve.

The second extreme gives too much freedom and requires implementing replay buffers...