Algorithms and matching lower bounds for s-sparse contextual bandits yield Õ((s/ε² + |A|/ε) log |Π|/δ) samples to output an ε-optimal policy.
Yan Dai, Qiwen Cui, and Simon S
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The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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The Sample Complexity of Multiclass and Sparse Contextual Bandits
Algorithms and matching lower bounds for s-sparse contextual bandits yield Õ((s/ε² + |A|/ε) log |Π|/δ) samples to output an ε-optimal policy.
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Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.