多臂赌博机问题(multi-armed bandit) 多臂赌博机是一个经典的问题。通常用来作为RL的入门级demo。所谓的k-armed bandit指的是这样一个任务:在你面前有一个类似老虎机的k个手柄的游戏机,每次选择并拉一个手柄,就会得到一个数值(可能是奖金金额),这个金额是一个随机数,它的分布对于每个手柄都是不同的,而你的任...
In stochastic bandits, a bit more clear how to compute our UCB. Same story, roughly, in contextual bandits – we can still compute UCB like estimates in this setting. Q: Why is RL from the contextual bandit setting? A1: Temporal connections. A2: Bootstrapping – do not get a sample of...
- MAB问题也在stochastic scheduling领域范畴中。Stochastic scheduling problems can be classified into three broad types: problems concerningthe scheduling of a batch of stochastic jobs,multi-armed banditproblems, andproblems concerning the scheduling of queueing systems. 基本问题 1. 有K台machine,每次选取其...
). Goal: Discuss on direction for UCB on action-values in RL, highlight some open questions and issues. Problem setting: Many model-free methods use uncertainty estimates: (1) Estimate uncertainty in Q(s, a), and (2) Reward bonuses or pseudo-counts. Let’s talk about (1) ...
This letter proposes a multi-armed bandit model-based vertical handoff approach (MABA). First, the vertical handoff problem is formulated as a multi-armed bandit problem. Then, the terminal services are divided into real-time services and non-real-time services, and their reward functions are ...
Research, Sunnyvale, CAAbstractWe provide a framework to exploit dependen-cies among arms in multi-armed bandit prob-lems, when the dependencies are in the formof a generative model on clusters of arms.We find an optimal MDP-based policy forthe discounted reward case, and also give an...
2001AbstractInthemulti-armedbanditproblem,agamblermustdecidewhicharmofnon-identicalslotmachinestoplayinasequenceoftrialssoastomaximizehisreward.Thisclassicalproblemhasreceivedmuchattentionbecauseofthesimplemodelitprovidesofthetrade-offbetweenexploration(tryingouteacharmtofindthebestone)andexploitation(playingthearm...
The stochastic multi-armed bandit model is a simpleion that has proven useful in many different contexts in statistics and machine learning. Whereas the achievable limit in terms of regret minimization is now well known, our aim is to contribute to a better understanding of the performance in te...
RobustControloftheMulti-armedBanditProblemFelipeCaro∗AparupaDasGupta†UCLAAndersonSchoolofManagementSeptember9,2015ForthcominginAnnalsofOperationsResearchhttp://dx.doi/10.1007/s10479-015-1965-7AbstractWestudyarobustmodelofthemulti-armedbandit(MAB)probleminwhichthetransitionprobabilitiesareambiguousandbelongto...
Federated multi-armed bandits(FMAB)是新的bandit范式,主要灵感来源于cognitive radio 和recommender systems的实际应用场景。这篇论文提出了一个通用型FMAB框架,并研究了该框架下的两种模型。 首先研究了近似模型,在该近似模型中,不同的local model都是global model 的服从于一个未知分布的随机实现。在这个近似模型中,...