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Poster
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha · Pierre Gaillard · Michal Valko

Thu Jul 16 06:00 AM -- 06:45 AM & Thu Jul 16 07:00 PM -- 07:45 PM (PDT) @ None #None
in Poster Session 41 »
In this paper, we consider the problem of sleeping bandits with stochastic action sets and adversarial rewards. In this setting, in contrast to most work in bandits, the actions may not be available at all times. For instance, some products might be out of stock in item recommendation. The best existing efficient (i.e., polynomial-time) algorithms for this problem only guarantee a $O(T^{2/3})$ upper-bound on the regret. Yet, inefficient algorithms based on EXP4 can achieve $O(\sqrt{T})$. In this paper, we provide a new computationally efficient algorithm inspired by EXP3 satisfying a regret of order $O(\sqrt{T})$ when the availabilities of each action $i \in \cA$ are independent. We then study the most general version of the problem where at each round available sets are generated from some unknown arbitrary distribution (i.e., without the independence assumption) and propose an efficient algorithm with $O(\sqrt {2^K T})$ regret guarantee. Our theoretical results are corroborated with experimental evaluations.
Keywords: Active Learning · Online Learning / Bandits · Online Learning, Active Learning, and Bandits

Author Information

Aadirupa Saha (Indian Institute of Science (IISc), Bangalore)
Pierre Gaillard
Michal Valko (DeepMind)

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