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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Tweaking wrappers

The final step in our sequence of experiments will be tweaking wrappers applied to the environment. This is very easy to overlook, as wrappers are normally written once, or just borrowed from other code, applied to the environment, and left to sit there. But you should be aware of their importance in terms of the speed and convergence of your method. For example, the normal DeepMind-style stack of wrappers applied to an Atari game looks like this:

  1. NoopResetEnv: applies a random amount of NOOP operations to the game reset. In some Atari games, this is needed to remove weird initial observations.
  2. MaxAndSkipEnv: applies max to N observations (four by default) and returns this as an observation for the step. This solves the "flickering" problem in some Atari games, when the game draws different portions of the screen on even and odd frames (a normal practice among 2600 developers to increase the complexity of the game's sprites).
  3. EpisodicLifeEnv...