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Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
Neural Information Processing Systems
In the latter, the algorithm estimates the low-rank matrix corresponding to the (state, action) value function of the current policy using the following two-phase procedure.
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Geometric Dataset Distancesvia Optimal Transport
Neural Information Processing Systems
Figur Pairwise compute exampleTPS tw and for , right). "Learningfromuncertaincurves: The 2-Wassersteinmetricfor Gaussian processes".
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