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Hands-On Neuroevolution with Python

You're reading from   Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838824914
Length 368 pages
Edition 1st Edition
Languages
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Author (1):
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Iaroslav Omelianenko Iaroslav Omelianenko
Author Profile Icon Iaroslav Omelianenko
Iaroslav Omelianenko
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods FREE CHAPTER
2. Overview of Neuroevolution Methods 3. Python Libraries and Environment Setup 4. Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
5. Using NEAT for XOR Solver Optimization 6. Pole-Balancing Experiments 7. Autonomous Maze Navigation 8. Novelty Search Optimization Method 9. Section 3: Advanced Neuroevolution Methods
10. Hypercube-Based NEAT for Visual Discrimination 11. ES-HyperNEAT and the Retina Problem 12. Co-Evolution and the SAFE Method 13. Deep Neuroevolution 14. Section 4: Discussion and Concluding Remarks
15. Best Practices, Tips, and Tricks 16. Concluding Remarks 17. Other Books You May Enjoy

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...

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