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Deep Q-Networks Our approach relies on Q-learning [19]. In Q-Learning Q∗(s, a) is the optimal value function for calculating the expected reward, for
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In the past, the reuse of the house and the change of function always resulted in a reduction of its Fig. 10.: The house in Arnoštov, offered for short-term rentals,
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The element comprises of three degrees of freedom per node: one for the normal displacement and the others for its partial derivatives
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