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Multi-Armed Bandits and Their Classical Algorithms: Strengths and Limitations [PDF]
ITM Web of ConferencesMulti-armed Bandits (MAB), which is the short form for multi- armed bandit, is playing a significant role in varied spaces including online advertising, recommendation system, clinical trials and medical decision- making and even in 5G. The model aims to
Wu Sichen
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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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Multi-armed Bandits with Compensation
CoRR, 2018 We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for $T$ steps. In each step, one short-term player arrives to the system. Upon arrival, the player aims to select an arm with the current best average reward and receives a stochastic reward associated ...
Siwei Wang 0002, Longbo Huang
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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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Multi-armed bandits in metric spaces [PDF]
Proceedings of the fortieth annual ACM symposium on Theory of computing, 2008 In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active ...
Robert Kleinberg +2 more
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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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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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Context-aware Multi-stakeholder Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system.
Tahereh Arabghalizi +1 more
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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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