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This leads to more robust and effective models, particularly in domains where the related options are not identified in advance. Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. Nonetheless, traditional RNNs suffer from the vanishing gradient downside, which limits their capability to learn long-range dependencies. This problem was
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