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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Things to try

If you are curious and want to experiment with this chapter's material on your own, then here is a short list of directions to explore. Be warned though: they can take lots of time and may cause you some moments of frustration during your experiments. However, these experiments are a very efficient way to really master the material from a practical point of view:

  • Try to take some other games from the Atari set, such as Breakout, Atlantis, or River Raid (my childhood favorite). This could require the tuning of hyperparameters.
  • As an alternative to FrozenLake, there is another tabular environment, Taxi, which emulates a taxi driver who needs to pick up passengers and take them to a destination.
  • Play with Pong hyperparameters. Is it possible to train faster? OpenAI claims that it can solve Pong in 30 minutes using the asynchronous advantage actor-critic method (which is a subject of part three of this book). Maybe it's possible with a DQN.
  • Can...