December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Objective function definition using the fitness score
In this section, you will learn about the creation of successful maze-solver agents using a goal-oriented objective function to guide the evolutionary process. This objective function is based on the estimation of the fitness score of the maze solver by measuring the distance between its final position and the maze exit after executing the 400 simulation steps. Thus, the objective function is goal-oriented and solely depends on the ultimate goal of the experiment: reaching the maze exit area.
In the next chapter, we will consider a different approach for solution search optimization, which is based on the Novelty Search (NS) optimization method. The NS optimization method is built around ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.