In the context of machine learning, best-arm identification can be viewed as a high-level abstraction and core component of active learn- ing, where the goal is to minimize the uncertainty of an un- derlying concept, and each step only reveals the label of the data point being queried.
Oct 2, 2023 · In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.
In this paper, we study the quantum speedup on a canonical task of reinforcement learning—best arm identification in multi-armed bandits. Multi-armed bandit ( ...
Dec 15, 2020 · In this paper, we give a comprehensive study of best-arm identification using quantum algorithms. Specifically, we obtain the following main ...
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A potential advantage of this oracle is that it can provide information about all arms when given an input that is a uniform superposition of all arm indices.
The second objective involves pure exploration with the goal of identifying the arm with the highest expected reward, i.e., Best Arm Identification (BAI) [26] .
The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed ...
Jan 21, 2021 · We consider the quantum version of the bandit problem known as best arm identification (BAI). We first propose a quantum modeling of the BAI ...
Our best-arm identification algorithm applies VTAA and VTAE to a variable time algorithm A. But what is A? Page 17. Variants: PAC, fixed budget, and non- ...
This paper describes recent advances in algorithms for identifying the arm with the highest mean in a stochastic multi- armed bandit (MAB) problem with high ...
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