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You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Python
Tools
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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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...
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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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