Reader small image

You're reading from  Hands-On Neuroevolution with Python.

Product typeBook
Published inDec 2019
Reading LevelExpert
PublisherPackt
ISBN-139781838824914
Edition1st Edition
Languages
Right arrow
Author (1)
Iaroslav Omelianenko
Iaroslav Omelianenko
author image
Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko

Right arrow

Objective function for a single-pole balancing experiment

Our goal is to create a pole balancing controller that's able to maintain a system in a stable state within defined constraints for as long as possible, but at least for the expected number of time steps specified in the experiment configuration (500,000). Thus, the objective function must optimize the duration of stable pole-balancing and can be defined as the logarithmic difference between the expected number of steps and the actual number of steps obtained during the evaluation of the phenotype ANN. The loss function is given as follows:

In this experiment, is the expected number of time steps from the configuration of the experiment, and is the actual number of time steps during which the controller was able to maintain a stable pole balancer state within allowed bounds (refer to the reinforcement signal definition...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Hands-On Neuroevolution with Python.
Published in: Dec 2019Publisher: PacktISBN-13: 9781838824914

Author (1)

author image
Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko