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

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

RL's complications

The first thing to note is that observation in RL depends on an agent's behavior and, to some extent, it is the result of this behavior. If your agent decides to do inefficient things, then the observations will tell you nothing about what it has done wrong and what should be done to improve the outcome (the agent will just get negative feedback all the time). If the agent is stubborn and keeps making mistakes, then the observations will give the false impression that there is no way to get a larger reward—life is suffering—which could be totally wrong.

In ML terms, this can be rephrased as having non-i.i.d. data. The abbreviation i.i.d. stands for independent and identically distributed, a requirement for most supervised learning methods.

The second thing that complicates our agent's life is that it needs to not only exploit the knowledge it has learned, but actively explore the environment, because maybe doing things differently...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

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

RL's complications

Unlock this book and the full library FREE for 7 days

Author (1)

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