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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

Pole-Balancing Experiments

In this chapter, you will learn about a classic reinforcement learning experiment, which is also an established benchmark for testing various implementations of the control strategies. In this chapter, we consider three modifications of the cart-pole balancing experiment and develop control strategies that can be used to stabilize the cart-pole apparatuses of given configurations. You will learn how to write accurate simulations of real-life physical systems and how to use them for a definition of the objective function for the NEAT algorithm. After this chapter, you will be ready to apply the NEAT algorithm to implement controllers that can be directly used to control physical appliances.

In this chapter, we will cover the following topics:

  • The single-pole balancing problem in reinforcement learning
  • Implementation of the simulator of the cart-pole...
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