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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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Toc

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

Running the experiment with a simple maze configuration

We start our experiments related to the creation of the successful maze navigation agent with a simple maze configuration. The simple maze configuration, while having the deceptive local optima cul-de-sacs discussed earlier, has a relatively straightforward path from the start point to the exit point.

The following diagram represents the maze configuration used for this experiment:

The simple maze configuration

The maze in the diagram has two specific positions marked with filled circles. The top-left circle denotes the starting position of the maze navigator agent. The bottom-right circle marks the exact location of the maze exit that needs to be found by the maze solver. The maze solver is required to reach the vicinity of the maze exit point denoted by the specific exit range area around it in order to complete the task...

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