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ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
Algorithm configuration procedures optimize parameters of a given algorithm to perform well over a distribution of inputs.
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
The construction by Du et al. (2019) implies that even if a learner is given linear features in $\mathbb R^d$ that approximate the rewards in a bandit with a uniform error of $\epsilon$, then searching for an action that is optimal up to $O(\epsilon)$ requires examining essentially all actions.
Exploration-Enhanced POLITEX
POLITEX has sublinear regret guarantees in uniformly-mixing MDPs when the value estimation error can be controlled, which can be satisfied if all policies sufficiently explore the environment.
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution.
