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Evolving Echo State Networks for Minimally Cognitive Unsupervised Learning Tasks
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science.
2013 (English)MasteroppgaveStudent thesis
Abstract [en]

This thesis investigates the use of Echo State Networks (ESNs) in unsupervised learning environments, by employing Evolutionary Algorithms (EAs) to evolve ESNs to control an agent that performs a novel, minimally-cognitive learning task. The task employed in this thesis is a modified version of the classic video game Frogger. ESNs are investigated since they promise to combine the temporal abilities seen in other Recurrent Neural Networks (RNNs) with a straightforward method to train the network. However, previous work employing ESNs in unsupervised environments is lacking. The evolved ESNs are compared to feed-forward Artificial Neural Networks (ANNs) as well as ESNs trained with regular supervised learning, in a comparative performance measure to find out which method is best suited to control Frogger. The results from this thesis show that not only do ESNs work well with EAs, but they surpass traditional feed-forward ANNs on the Frogger task. Additionally, it is shown that for the Frogger task, evolved ESNs also outperform ESNs trained with supervised learning. The results from this work should serve as an encouragement to use ESNs for more tasks in the future, due to their competitive performance and ease of setup.

Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2013. , 100 p.
URN: urn:nbn:no:ntnu:diva-22966Local ID: ntnudaim:8904OAI: diva2:655600
Available from: 2013-10-12 Created: 2013-10-12 Last updated: 2013-10-12Bibliographically approved

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