Risk-aware multi-armed bandit problem with application to portfolio selection. [PDF]
R Soc Open Sci, 2017 Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem ...
Huo X, Fu F.
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Decision making for large-scale multi-armed bandit problems using bias control of chaotic temporal waveforms in semiconductor lasers. [PDF]
Sci Rep, 2022 Decision making using photonic technologies has been intensively researched for solving the multi-armed bandit problem, which is fundamental to reinforcement learning.
Morijiri K +4 more
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Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance. [PDF]
Sensors (Basel), 2022 Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution.
Almarzoqi SA, Yahya A, Matar Z, Gomaa I.
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Some performance considerations when using multi-armed bandit algorithms in the presence of missing data. [PDF]
PLoS One, 2022 When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according ...
Chen X, Lee KM, Villar SS, Robertson DS.
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Multi-Armed Bandit-Based User Network Node Selection. [PDF]
Sensors (Basel)In the scenario of an integrated space–air–ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a
Gao Q, Xie Z.
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Reinforcement learning framework for computerized adaptive testing using multi armed bandit approach. [PDF]
Sci RepThis research introduces a new framework based on the combination of deep learning and reinforcement learning techniques for computer-based adaptive testing that overcomes the limitations of traditional methods for test preparation.
Tang B, Li S, Zhao C.
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Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm. [PDF]
Entropy (Basel), 2021 Cavenaghi E +3 more
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Design of Multi-Armed Bandit-Based Routing for in-Network Caching
IEEE Access, 2023 This paper proposes a multi-armed bandit-based routing method for in-network cache-enabled networks. In-network caching enables intermediate routers to store contents in their cache, which is adopted in new networking paradigms such as Information ...
Gen Tabei +3 more
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Multi-objective contextual bandits in recommendation systems for smart tourism. [PDF]
Sci RepIn the context of smart tourism, recommender systems play a pivotal role in enhancing the personalization and quality of travel experiences. Tourists often face challenges in decision-making due to information overload.
Qassimi S, Rakrak S.
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Applied Sciences, 2022 In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss.
So Hasegawa +6 more
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