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

Product typeBook
Published inDec 2019
Reading LevelExpert
PublisherPackt
ISBN-139781838824914
Edition1st Edition
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Author (1)
Iaroslav Omelianenko
Iaroslav Omelianenko
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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

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The single-pole balancing problem

The single-pole balancer (or inverted pendulum) is an unstable pendulum that has its center of mass above its pivot point. It can be stabilized by applying external forces under the control of a specialized system that monitors the angle of the pole and moves the pivot point horizontally back and forth under the center of mass as it starts to fall. The single-pole balancer is a classic problem in dynamics and control theory that is used as a benchmark for testing control strategies, including strategies based on reinforcement learning methods. We are particularly interested in the implementation of the specific control algorithm that uses neuroevolution-based methods to stabilize the inverted pendulum for a given amount of time.

The experiment described in this chapter considers the simulation of the inverted pendulum implemented as a cart that...

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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