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The paper is discussing binary classification target values only. Sometimes binary classification target values are encoded as either -1 or 1. These are the asymptotic limits of a logit-type function. Instead, it might be empirically more useful to encode binary classification target values to either -0.9 or .9.


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Loss is computed for each sample and then averaged over the entire mini-batch and the weights are updated once. Check this video (start from 6:11) for details.


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These are a few approaches I found in the research field that combines both RNN and Reinforcement Learning that looks promising Reinforcement learning with LSTM networks Reinforcement learning with RNN Hybrid RNN approach Research paper links A Reinforcement Learning and Recurrent Neural Network Based Dynamic User Modeling System Supervised Reinforcement ...


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After weeks trying to get into the very depth of the question as someone who's not from a maths background, neither data science, I figured out how it works internally. This is one, raw way to make it work, and my implementation, haven't seen it anywhere implemented like this, I'm pretty sure there are more modern ways to do it, so if you want, use it with ...


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