587 research outputs found
Satisficing in multi-armed bandit problems
Satisficing is a relaxation of maximizing and allows for less risky decision
making in the face of uncertainty. We propose two sets of satisficing
objectives for the multi-armed bandit problem, where the objective is to
achieve reward-based decision-making performance above a given threshold. We
show that these new problems are equivalent to various standard multi-armed
bandit problems with maximizing objectives and use the equivalence to find
bounds on performance. The different objectives can result in qualitatively
different behavior; for example, agents explore their options continually in
one case and only a finite number of times in another. For the case of Gaussian
rewards we show an additional equivalence between the two sets of satisficing
objectives that allows algorithms developed for one set to be applied to the
other. We then develop variants of the Upper Credible Limit (UCL) algorithm
that solve the problems with satisficing objectives and show that these
modified UCL algorithms achieve efficient satisficing performance.Comment: To appear in IEEE Transactions on Automatic Contro
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