Precision Agriculture Optimization based on Multi-Armed Bandits Algorithm: Wheat Yield Optimization under Different Temperature and Precipitation Conditions [PDF]
ITM Web of ConferencesClimate change and the growing unpredictability of environmental elements such as temperature and precipitation present considerable challenges to contemporary agriculture. Data-driven algorithms present promising solutions by offering more precise tools
Huang Qikang
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Distributed Exploration in Multi-Armed Bandits
CoRR, 2013 We study exploration in Multi-Armed Bandits in a setting where $k$ players collaborate in order to identify an $ε$-optimal arm. Our motivation comes from recent employment of bandit algorithms in computationally intensive, large-scale applications. Our results demonstrate a non-trivial tradeoff between the number of arm pulls required by each of the ...
Eshcar Hillel +4 more
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Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
, 2021 Konstantinos Sfikas +2 more
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Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms [PDF]
, 2023 Tiancheng Jin, Junyan Liu, Haipeng Luo
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MathematicsThe multi-armed bandit (MAB) problem is a foundational model for sequential decision-making under uncertainty. While MAB has proven valuable in applications such as clinical trials and online advertising, traditional formulations have limitations ...
Ali Baheri
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Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health [PDF]
, 2023 Jackson A. Killian +5 more
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Active Learning in Multi-armed Bandits [PDF]
, 2008 In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which option to generate the next sample from in order to produce estimates with equally good precision for all the distributions.
András Antos +2 more
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Batched Multi-armed Bandits Problem
CoRR, 2019 To appear in NeurIPS 2019 as an oral ...
Zijun Gao +3 more
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Visualizations for interrogations of multi‐armed bandits
Stat, 2019 A multi‐armed bandit (MAB) algorithm is a sequential experimentation procedure on multiple treatments, which explores their effects and exploits the seemingly optimum treatment. An algorithm is selected for a particular context by evaluating the performances of multiple candidate algorithms in controlling the regret of exploration versus exploitation ...
Timothy J. Keaton, Arman Sabbaghi
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EvoClusterBandit: Adaptive Partitioned Bandit Algorithm for Dynamic Environments with Latent Variable Modeling [PDF]
ITM Web of ConferencesExisting multi-armed bandit algorithms struggle in dynamic environments with latent shifts such as abrupt changes in user behavior or contextual features.
Ren Hedong
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