Ionic Decision-maker for Solving Multi-armed Bandit Problems
نویسندگان
چکیده
منابع مشابه
Algorithms for multi-armed bandit problems
The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important...
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We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward va and can choose an amount xa to pass on to the principal (keeping va−xa for itself). All non-pulled arms get reward 0. Each strategic arm tries to maximize its own utility over the cour...
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In a multi-armed bandit problem, at each time step, an algorithm chooses one of the possible arms and observes its rewards. The goal is to maximize the sum of rewards over all time steps (or to minimize the regret). In the conventional formulation of the problem, the algorithm has no prior knowledge about the arms. Many applications, however, provide some data about the arms even before the alg...
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ژورنال
عنوان ژورنال: Journal of The Surface Finishing Society of Japan
سال: 2020
ISSN: 0915-1869,1884-3409
DOI: 10.4139/sfj.71.453