1. 贪心算法(Greedy Algorithm):总是选择当前已知期望奖励最高的老虎机臂。2. ε-贪心算法(Epsilon-Greedy Algorithm):大多数时间选择当前已知期望奖励最高的老虎机臂,但以小概率ε随机选择其他老虎机臂进行探索。3. UCB(Upper Confidence Bound)算法:选择具有最高上置信界(即当前估计的期望奖励加上一个信...
[6] Nie, G., Agarwal, M., Umrawal, A. K., Aggarwal, V., & Quinn, C. J. (2022, February). An Explore-then-Commit Algorithm for Submodular Maximization Under Full-bandit Feedback. In The 38th Conference on Uncertainty in Artificial Intelligence. [7] Gabillon, V., Kveton, B., We...
2. K-armed Bandit Problem 2.1 问题设置 多臂赌博机问题(Multi-armed Bandit Problem)也叫K臂赌博机,它是一个经典的决策问题,它的具体设置如下: 一个赌博机,有K个摇杆,每摇动一个摇杆会获得一个reward(reward是一个固定均值,方差非零的随机变量),问如何在有限的次数下选择摇动摇杆的策略会使得累计reward最大。
2.8梯度赌博机算法(Gradient Bandit Algorithm) 到目前为止我们使用方法来估计value,并用action value的估计值来选择action,这些方法一般是个好方法,但不是唯一的。在这一节中我们用Ht(a)来表示该action的数值倾向,倾向越大,该action就越容易被选择,但是倾向与result没有直接关系。 soft—max distribution(选择依据) ...
在讨论算法之前,首先要明确几种bandit model。根据对于reward过程的不同假设,主要可以分为三种类型:Stochastic,AdversarialandMarkovian。几种经典的策略与之对应, UCB algorithm for the stochastic case, Exp3 randomized algorithm for theadversarial case, so-called Gittins indices for the Markovian case.[4] ...
整理得到 A simple bandit algorithm 对于非固定回报的多臂赌博机问题,每个手臂的回报不能用上面的形式估计平均值,而是改写为 又可被称为 exponential recency-weighted average,不难看出最新的回报估计是过去回报和最近回报的加权混合。 其中学习步长满足以下条件可以保证收敛 ...
This is an umbrella project for several related efforts at Microsoft Research Silicon Valley that address various Multi-Armed Bandit (MAB) formulations motivated by web search and ad placement. The MAB problem is a classical paradigm in Machine Learning in which an online algorithm chooses from a ...
Multi-armed bandit algorithmAdaptive learningExploration and exploitationPersonalized learningAdaptive learning aims to provide each student individual tasks specifically tailed to his/her strengths and weaknesses. However, it is challenging to realize it, overcoming the complexity issue in online learning. ...
identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. These properties are not described by current theory, even though they can be exploited in practice in the design of heuristics. Thirdly, the algorithms’ perfor- mance relative each to ...
Empirically, algorithms that use this kind of algorithm seem to work quite well: (1) Bootstrap DQN, (2) Bayesian DQN, (3) Double Uncertain Value Networks, (4) UCLS (new algo in this work).Conduct experiments in a continuous variant of the River Swim domain. UCLS and ...