For improving multi-armed bandits, randomized algorithms achieve a near-tight Θ~(√k) worst-case competitive ratio, and polynomially many historical instances suffice to tune a curvature parameter; pessimism traps and grit are modeled as sequential decision problems.
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Algorithmic Approaches to Sequential Decision-Making and Social Epistemology
For improving multi-armed bandits, randomized algorithms achieve a near-tight Θ~(√k) worst-case competitive ratio, and polynomially many historical instances suffice to tune a curvature parameter; pessimism traps and grit are modeled as sequential decision problems.