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Toward Evolving Robust, Deliberate Motion Planning With HyperNEAT

Abstract

Previous works have used a novel hybrid network architecture to create deliberative behaviours to solve increasingly challenging tasks in two-dimensional and threedimensional artificial worlds. At the foundation of each is a static hand-designed neural network for robust and deliberative motion planning. This paper presents results from replacing the hand-designed motion-planning subnetwork with HyperNEAT. Simulations are run on the original two-dimensional world with, and without, relative position inputs and a multievaluation fitness function, thus assessing the relative performance of each strategy. The focus of this work is on solutions adaptable to general environments; following evolution, each strategy's performance is evaluated on 10,000 world configurations. The results demonstrate that although HyperNEAT was not able to achieve as robust results as a hand-design approach, the best strategy was comparable, with just a 3-4% drop in performance. Relative position inputs and the multievaluation fitness function were both significant in achieving superior general performance, compared to those simulations without.

Acceptance Date Sep 30, 2017
Publication Date Feb 26, 2018
Pages 3488 -3495
Series Title IEEE Symposium Series on Computational Intelligence 2017 (IEEE SSCI 2017)
Book Title Proceedings of the IEEE Symposium Series on Computational Intelligence 2017
ISBN 978-1-5386-2725-9