Restless multi-armed bandit
WebAuthors. Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe. Abstract. We propose and study Collapsing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when an arm is played, the state is fully observed, thus“collapsing” any uncertainty, but … WebRestless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide …
Restless multi-armed bandit
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WebJun 16, 2013 · We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where simple arms with unknown distributions form super arms.In each round, a super arm is played and the outcomes of its related simple arms are observed, which helps the selection of super arms in future rounds. WebThe Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action …
WebNov 22, 2010 · The restless multi-armed bandit probl em has a broad range of applications. For example, in a. cognitive radio network, a secondary user searches among several … WebWhat is the multi-armed bandit problem? In marketing terms, a multi-armed bandit solution is a ‘smarter’ or more complex version of A/B testing that uses machine learning …
WebDec 30, 2024 · Photo by Carl Raw on Unsplash. Multi-armed bandit problems are some of the simplest reinforcement learning (RL) problems to solve. We have an agent which we … WebA player competes with multiple agents. Each bandit has a payoff that changes with a probability p c per round. The agents and player choose one of three options: (1) Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among n I …
WebSep 20, 2024 · We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled …
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