General state/action space. Agent estimates action-values from stream of interaction. How can the agent be confident in its estimates of Q∗ (s, a). Our goal: directed exploration to efficiently estimate Q∗ (s, a). Many model-free methods use uncertainty estimates: (1) Estimate uncerta...
We study a distributed decision-making problem in which multiple agents face the same multi-armed bandit (MAB), and each agent makes sequential choices among arms to maximize its own individual reward. The agents cooperate by sharing their estimates over a fixed communication graph. We consider ...
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...
Then a decentralized multi-agent multi-armed bandit (MAMAB) algorithm is developed for each SBS to decide its own cache strategy based jointly on its past observations and estimated upcoming cache action of other SBSs. This decentralized MAMAB algorithm with $\epsilon $ -calibration enables ...
Specifically, we develop and utilize the multi-agent multi-armed bandit (MAB) problem to model and study how multiple interacting agents make decisions that balance the explore-exploit tradeoff. we consider several different communication protocols for sharing information between agents. We develop and ...
从问题入手: 1.1 问题描述:Muti-arm Bandits Muti-armed Bandits(多臂老虎机)问题,也叫K-armed Bandit Problem... value) q_estimate是一个1*10的列表,记录agent对每一个老虎机价值的估计值 act()方法是依据算法(我们稍后会探讨这部分内容)选择合适的行动(即选择几号老虎机) step 推荐系统遇上深度学习(十二...
“multi-armedbandit”namecomes fromenvisioningacasinowithachoiceofK“one-armed bandit”slotmachines.Ineachtrial,anagentcanpulloneof thearmsandreceiveitsassociatedpayoff,butdoesnotlearn whatpayoffsitmighthavereceivedfromotherarms.Over asequenceoftrials,theagent’sgoalistomixexploration tolearnwhicharmsprovide...
它使用self-attention来迭代推理场景中实体之间的关系,并指导无模型策略。我们的结果表明,在一种名为Box-World的新的导航和规划任务中,我们的agent找到了可解释的解决方案,这些解决方案在样本复杂性、泛化到比训练期间所经历的更复杂的场景的能力以及整体性能方面都有......
摘要: Multi-armed bandit (MAB) problems are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative (competing) projects. Such problems are paradigms of a fundamental conflict between making decisions (allocating resources) that yield...
这里我们讨论上述不等式的一个应用场景,也是强化学习里的一类经典子问题: 多臂老虎机问题(stochastic multi-armed bandits),后续里统称MAB问题。 一个最初的多臂老虎机问题[1]可以描述如下: 一个玩家走进一个赌场,赌场里有K个老虎机,每个老虎机的期望收益不一样。假设玩家总共可以玩$T$轮, 在每一轮中,玩家可以...