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

Evolution strategies

One subset of black-box optimization methods is called evolution strategies (ES), and it was inspired by the evolution process. With ES, the most successful individuals have the highest influence on the overall direction of the search. There are many different methods that fall into this class, and in this chapter, we will consider the approach taken by the OpenAI researchers Tim Salimans, Jonathan Ho, and others in their paper, Evolution Strategies as a Scalable Alternative to Reinforcement Learning [1], published in March 2017.

The underlying idea of ES methods is simple: on every iteration, we perform random perturbation of our current policy parameters and evaluate the resulting policy fitness function. Then, we adjust the policy weights proportionally to the relative fitness function value.

The concrete method used in the paper is called covariance matrix adaptation evolution strategy (CMA-ES), in which the perturbation performed is the random noise...

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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

Evolution strategies

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Author (1)

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