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

Black-box methods

To begin with, let's discuss the whole family of black-box methods and how it differs from what we've covered so far. Black-box optimization methods are the general approach to the optimization problem, when you treat the objective that you're optimizing as a black box, without any assumptions about the differentiability, the value function, the smoothness of the objective, and so on. The only requirement that those methods expose is the ability to calculate the fitness function, which should give us the measure of suitability of a particular instance of the optimized entity at hand.

One of the simplest examples in this family is random search, which is when you randomly sample the thing you're looking for (in the case of RL, it's the policy, ), check the fitness of this candidate, and if the result is good enough (according to some reward criteria), then you're done. Otherwise, you repeat the process again and again. Despite the simplicity...

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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

Black-box methods

Unlock this book and the full library FREE for 7 days

Author (1)

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