Results 41 to 50 of about 3,478,919 (201)
Best Agent Identification for General Game Playing
IEEE AccessWe present an efficient and generalised procedure to accurately identify the best (or near best) performing algorithm for each sub-task in a multi-problem domain.
Matthew Stephenson +3 more
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IEEE Access, 2018 The problem of rested and restless multi-armed bandits with constrained availability (RMAB-CA) of arms is considered. The states of arms evolve in Markovian manner and the exact states are hidden from the decision maker. First, some structural results on
Varun Mehta +4 more
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Reinforcement Learning Based Beamforming Jammer for Unknown Wireless Networks
IEEE Access, 2020 A jamming attack refers to adversarial activities to cause an interruption of communication among legitimate nodes in a wireless network by transmitting a jamming signal.
Gyungmin Kim, Hyuk Lim
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CoRR, 2018 We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter. The arms are divided into different groups, each of which has a common parameter. Therefore, when the player selects an arm at each time slot, information of other arms in the same group is also revealed.
Zhiyang Wang, Ruida Zhou, Cong Shen 0001
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Multi-armed Bandits with Cost Subsidy
CoRR, 2020 In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards. We present two applications, intelligent SMS routing problem and ad audience optimization
Deeksha Sinha +3 more
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COM-MABs: From Users' Feedback to Recommendation
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Recently, the COMbinatorial Multi-Armed Bandits (COM-MAB) problem has arisen as an active research field. In systems interacting with humans, those reinforcement learning approaches use a feedback strategy as their reward function.
Alexandre Letard +3 more
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Budgeted Combinatorial Multi-Armed Bandits
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 We consider a budgeted combinatorial multi-armed bandit setting where, in every round, the algorithm selects a super-arm consisting of one or more arms. The goal is to minimize the total expected regret after all rounds within a limited budget. Existing techniques in this literature either fix the budget per round or fix the number of arms pulled in ...
Debojit Das +2 more
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Combinatorial online learning based on optimizing feedbacks
大数据, 2021 Combinatorial online learning studies how to learn the unknown parameters and gradually find the optimal combination of targets during the interactions with the environment.This problem has a wide range of applications including advertisement placement ...
Fang KONG +3 more
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Enhancing Dynamic Movie Recommendations With User Expectation Ratings in Contextual Bandit Models [PDF]
ITM Web of ConferencesModern movie recommendation systems face challenges such as dynamic personalization and real-time adaptability. Traditional methods like collaborative filtering and content-based recommendations struggle with dynamic user preferences and cold-start ...
Sun Weiye
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Multi-Armed Bandits with Interference
CoRRExperimentation with interference poses a significant challenge in contemporary online platforms. Prior research on experimentation with interference has concentrated on the final output of a policy. The cumulative performance, while equally crucial, is less well understood. To address this gap, we introduce the problem of {\em Multi-armed Bandits with
Su Jia, Peter I. Frazier, Nathan Kallus
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