Application examples
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Czibula and co-workers used Q-Learning in an environment (state-space) composed of all posible left, up, right and down moves to propose models for solving the 3D structure protein specified by its hydrophobicity and hydrophilicity
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Doğan and Ölmez have proposed a novel state space representation for predicting protein folds using a variant of Q-Learning
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Velloso and colleagues used several RL algorithms to attempt comparing the relationship between different genomes, represented as permuted blocks of genes
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Guimaraes and co-workers studied the relationship between leading and following cells in collective cell migration using Deep Q-Networks
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