In this paper, we propose a set of allocation strategies to deal with the multi-armed bandit problem, the possibilistic reward (PR) methods. First, we use possibilistic reward distributions to model the uncertainty about the expected rewards from the arm, derived from a set of infinite ...
This is an example of what’s called the multi-armed bandit problem, so named because a slot machine is informally called a one-armed bandit. The problem is not as whimsical as it might first seem. There are many important real-life problems, such as drug clinical trials, that are ...
Robust Control of the Multi-armed Bandit Problem Felipe Caro Aparupa Das Gupta UCLA Anderson School of Management September 9, 2015 Forthcoming in Annals of Operations Research http://dx.doi.org/10.1007/s10479-015-1965-7 Abstract We study a robust model of the multi-armed bandit (MAB) ...
improving the efficiency and fairness of explicit control protocol in multi-bottleneck networks:提高多瓶颈网络显控协议的效率与公平性 热度: 相关推荐 RobustControloftheMulti-armedBanditProblem FelipeCaro ∗ AparupaDasGupta † UCLAAndersonSchoolofManagement September9,2015 ForthcominginAnnalsofOperationsResea...
O. Madani, D. J. Lizotte, and R. Greiner. The budgeted multi-armed bandit problem. In the Proceedings of the Seventeenth Annual Conference on Learning Theory, (COLT), pages 643-645, 2004.Madani, O., Lizotte, D.J., Greiner, R.: The budgeted multi-armed bandit problem. In: Learning ...
One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve ...
James McCaffrey provides an implementation of the multi-armed bandit problem, which is not only interesting in its own right, it also serves as a good introduction to an active area of economics and machine learning research. Read article ...
We consider the classical multi-armed bandit problem with Markovian rewards. When played an arm changes its state in a Markovian fashion while it remains frozen when not played. The player receives a state-dependent reward each time it plays an arm. The
The Epsilon-Greedy /UCB ("upper confidence bound") for MAB (Multiarmed-bandit) problem sometime in reinforcement learning (RL) 你是球队教练,现在突然要打一场比赛,手下空降三个球员,场上只能有一个出战,你不知道他们的能力,只能硬着头皮上,如何根据有限的上场时间看出哪个球员厉害,然后多让他上,从而得...
Introduction and implementation of the strategies(include Thompson Sampling) for multi-armed bandit problem - ReactiveCJ/MultiArmedBandit