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Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
We show that the learned policy converges to the optimal policy in the order of $\tilde{O}(1/\sqrt{k}+\epsilon_{k, m})$ as the number of sub-sampled agents $k$ increases, where $\epsilon_{k, m}$ is the Bellman noise, by proving a novel generalization of the Dvoretzky-Kiefer-Wolfowitz inequality to the regime of sampling without replacement.
Identifying Chemicals Through Dimensionality Reduction
Civilizations have tried to make drinking water safe to consume for thousands of years.
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