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Hands-On Neuroevolution with Python.

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

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781838824914
Pages 368 pages
Edition 1st Edition
Languages
Author (1):
Iaroslav Omelianenko Iaroslav Omelianenko
Profile icon Iaroslav Omelianenko

Table of Contents (18) Chapters

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

Maze navigation problem

The maze navigation problem is a classic computer science problem that is closely related to creating autonomous navigation agents that can find a path through ambiguous environments. The maze environment is an illustrative domain for the class of problems that have a deceptive fitness landscape. This means that the goal-oriented fitness function can have steep gradients of fitness scores in dead ends in the maze that are close to the final goal point. Such areas of the maze become the local optima for objective-based search algorithms that may converge in these areas. When the search algorithm converges in such deceptive local optima, it cannot find an adequate maze-solver agent.

In the following example, you can see a two-dimensional maze with local optima dead ends, which are shaded in:

The two-dimensional maze configuration

The maze configuration in...

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