- We have included the hard-to-solve maze configuration into the experiment source code at https://github.com/PacktPublishing/Hands-on-Neuroevolution-with-Python/blob/master/Chapter9/hard_maze.txt. You can try to solve the hard maze configuration by using the following command: python maze_experiment_safe.py -g 120 -t 5 -m hard --width 200 --height 200.
- We have found a successful solution using 1571021768 as a random seed value. Try to find another random seed value producing a successful solution. How many generations did it take to find it?
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Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
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Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko