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arXiv · 1803.10415

A Better Resource Allocation Algorithm with Semi-Bandit Feedback

Yuval Dagan·Koby Crammer
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Abstract

We study a sequential resource allocation problem between a fixed number of arms. On each iteration the algorithm distributes a resource among the arms in order to maximize the expected success rate. Allocating more of the resource to a given arm increases the probability that it succeeds, yet with a cut-off. We follow Lattimore et al. (2014) and assume that the probability increases linearly until it equals one, after which allocating more of the resource is wasteful. These cut-off values are fixed and unknown to the learner. We present an algorithm for this problem and prove a regret upper bound of $O(\log n)$ improving over the best known bound of $O(\log^2 n)$. Lower bounds we prove show that our upper bound is tight. Simulations demonstrate the superiority of our algorithm.

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Yuval Dagan, Koby Crammer. 2018-03-28. A Better Resource Allocation Algorithm with Semi-Bandit Feedback. https://arxiv.org/abs/1803.10415

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