December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Running the experiment with a hard-to-solve maze configuration
The next experiment in this chapter is to run the neuroevolution process to find an agent that can solve a maze with a more complex configuration of walls. This hard-to-solve maze configuration introduces powerful local fitness optima traps and does not have a straightforward route from the start position of the agent to the exit area of the maze. You can see the maze configuration in the following diagram:
The hard-to-solve maze configuration
The maze configuration has its start position in the bottom-left corner, marked with a green circle, and the position of the maze exit point ...
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