Evolving Indirectly Encoded Convolutional Neural Networks to Play Tetris With Low-Level Features
Item
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Title
Evolving Indirectly Encoded Convolutional Neural Networks to Play Tetris With Low-Level Features
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Identifier
Jacob Schrum. 2018. Evolving indirectly encoded convolutional neural networks to play tetris with low-level features. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 205-212. DOI: https://doi.org/10.1145/3205455.3205459
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uri
https://collections.southwestern.edu/s/suscholar/item/240
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Abstract
Tetris is a challenging puzzle game that has received much attention from the AI community, but much of this work relies on intelligent high-level features. Recently, agents played the game using low-level features (10 X 20 board) as input to fully connected neural networks evolved with the indirect encoding HyperNEAT. However, research in deep learning indicates that convolutional neural networks (CNNs) are superior to fully connected networks in processing visuospatial inputs. Therefore, this paper uses HyperNEAT to evolve CNNs. The results indicate that CNNs are indeed superior to fully connected neural networks in Tetris, and identify several factors that influence the successful evolution of indirectly encoded CNNs.
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Publisher
Proceedings of the Genetic and Evolutionary Computation Conference
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Subject
Computing methodologies
Machine learning
Machine learning approaches
Neural networks
Bio-inspired approaches
Generative and developmental approaches