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Optimizing Data Filtering in Multi-Armed Bandit Algorithms for Reinforcement Learning [PDF]
ITM Web of ConferencesThis study investigates the performance of data filtering algorithms in multi-armed bandit (MAB) problems for reinforcement learning applications. It focuses on five algorithms: Epsilon-Greedy (ε- Greedy), Upper Confidence Bound (UCB), Linear Upper ...
Zhang Shengshi
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Thompson Sampling vs Ols: Analyzing Tariff Impact on Export Decisions [PDF]
ITM Web of ConferencesThis paper, based on the Melitz model, examines how effective trade costs influenced European Union (EU) steel exports to the U.S. between 2008 and 2024.
Zhang Handong
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Thompson Sampling-Based Channel Selection Through Density Estimation Aided by Stochastic Geometry
IEEE Access, 2020 We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated ...
Wangdong Deng +4 more
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Thompson Sampling for Bandits with Clustered Arms [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. We show, both theoretically and empirically, how exploiting a given cluster structure can significantly improve the regret and computational cost ...
Emil Carlsson +2 more
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A Reinforcement Learning Approach for Smart Farming [PDF]
Database Systems Journal, 2019 At a basic level, the aim of machine learning is to develop solutions for real-life engineering problems and to enhance the performance of different computers tasks in order to obtain an algorithm that is highly independent of human intervention.
Gabriela ENE
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Optimizing Station-Based Shared E-Bike Allocation Using Multi-Armed Bandit Algorithms [PDF]
ITM Web of ConferencesIn the era of rapid urban transformation, the sharing economy has revolutionized transportation solutions, with shared bicycles emerging as a prominent example.
Zhao Wenkui
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Bootstrap Thompson Sampling and Sequential Decision Problems in the Behavioral Sciences
SAGE Open, 2019 Behavioral scientists are increasingly able to conduct randomized experiments in settings that enable rapidly updating probabilities of assignment to treatments (i.e., arms).
Dean Eckles, Maurits Kaptein
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Journal of Forest Science, 2012 Based on two species of Coastal Mangrove in Hainan of China, Sonneratia Apetala Buch-Ham and Sonneratia caseoli, we estimated the density of the two species to evaluate the efficiency of adaptive cluster sampling (ACS), simple random sampling (SRS) and ...
Y. Lei, J. Shi, T. Zhao
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Jackknife Replication Variance Estimation of Population Total Under Systematic Sampling With Varying Probabilities [PDF]
Matrix Science Mathematic, 2017 Systematic sampling with probability proportional to size either randomized or non-randomized, has been used in a number of large scale household survey. Even this sampling technique has been widely used in forestry for the estimation of volume of timber.
Khadija Fatima +2 more
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On two-stage adaptive cluster sampling to assess pest density
浙江大学学报. 农业与生命科学版, 2000 The adaptive cluster sampling introduced by Thompson is a powerful method for a survey of a population which is highly clumped with clumps widely separated.
ZHANG Nan-song +2 more
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