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Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
This work proposes the SUB-SAMPLE-Q algorithm where the global agent subsamples $k\leq n$ local agents to compute an optimal policy in time that is only exponential in $k$, providing an exponential speedup from standard methods that are exponential in $n$.
Identifying Chemicals Through Dimensionality Reduction
Civilizations have tried to make drinking water safe to consume for thousands of years.
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