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• W2306408527 abstract "A central problem to decision making under uncertainty is the trade-o between exploration and exploitation: between learning from and adapting to a stochastic system and exploiting the current best-knowledge about the system. A fundamental decision-theoretic model that captures this trade-o is the celebrated stochastic Multi-arm Bandit Problem. In this paper, we consider scenarios where the exploration phase corresponds to designing experiments, and the exploration phase has the following restrictions: (1) it must necessarily precede the exploitation phase; (2) it is expensive in terms of some resource consumed, so that only a limited amount of exploration can be performed; and (3) switching from one experiment to another incurs a setup cost. Such a model, which is termed budgeted learning, is relevant in scenarios such as clinical trials and sensor network data acquisition. Though the classic multi-armed bandit problem admits to a polynomial time greedy optimal solution termed the Gittins index policy, the budgeted learning problem does not admit to such a greedy optimal solution. In fact, the problem is NP-Hard even in simple settings. Our main contribution is in presenting constant factor approximation algorithms for this problem via a novel linear program rounding technique based on stochastic packing." @default.
• W2306408527 created "2016-06-24" @default.
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• W2306408527 date "2007-01-01" @default.
• W2306408527 modified "2023-09-24" @default.
• W2306408527 title "Multi-armed Bandits with Limited Exploration" @default.
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