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

The theoretical background of the cross-entropy method

This section is optional and included for readers who are interested in why the method works. If you wish, you can refer to the original paper on the cross-entropy method, which will be given at the end of the section.

The basis of the cross-entropy method lies in the importance sampling theorem, which states this:

In our RL case, H(x) is a reward value obtained by some policy, x, and p(x) is a distribution of all possible policies. We don't want to maximize our reward by searching all possible policies; instead we want to find a way to approximate p(x)H(x) by q(x), iteratively minimizing the distance between them. The distance between two probability distributions is calculated by Kullback-Leibler (KL) divergence, which is as follows:

The first term in KL is called entropy and it doesn't depend on p₂(x), so it could be omitted during the minimization. The second term is called cross-entropy, which is...

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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.
Read more about Maxim Lapan

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

The theoretical background of the cross-entropy method

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Author (1)

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