1、问题介绍:k-armed Bandit Problem Multi-armed bandit原本是从赌场中的多臂老虎机的场景中提取出来的数学模型,其中 arm 指的是老虎机(slot machine)的拉杆,bandit 是多个拉杆的集合,bandit=arm1,arm2,……,armkbandit=arm1,arm2,……,armk。每个 bandit setting 对应一个回报函数(reward function),现在需要...
我们继续来看一下组合多臂老虎机(CMAB)问题。在组合多臂老虎机问题中,你一次拉动的不是一个臂,而是多个臂组成的集合,我们称之为超臂(super arm),原来的每个臂我们称之为基准臂(base arm),以示区别。拉完这个超臂后,超臂所包含的每个基准臂会给你一个反馈,而这个超臂整体也给你带来某种复合的收益。 组合...
在强化学习中,多臂老虎机常常作为一个简化的理想模型而被讨论。 多臂老虎机的基本设定如下:假设总共有K个臂(Arm),每个臂a都有一个未知的奖励分布(为了简化起见,我们假设奖励服从未知参数θa的伯努利分布,当然,也可以是其他更复杂的分布),每次拉动一个臂a,我们会得到一个奖励R,R∼Bernoulli(θa)。我们的目标...
A multi-armed bandit (MAB) problem is described as follows. At each time-step, a decision-maker selects one arm from a finite set. A reward is earned from this arm and the state of that arm evolves stochastically. The goal is to determine an arm-pulling policy that maximizes expected ...
Greedy算法1.从问题入手: 1.1问题描述:Muti-armBanditsMuti-armedBandits(多臂老虎机)问题,也叫K-armedBanditProblem... 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 ...
There are many different solutions that computer scientists have developed to tackle the multi-armed bandit problem. Below is a list of some of the most commonly used multi-armed bandit solutions:Epsilon-greedy This is an algorithm for continuously balancing exploration with exploitation. (In ‘...
We model the RMAB problem as a finite-state, infinite horizon robust MDP in which the payoffs are discounted by δ∈ (0, 1) in each period and the reward obtained for pulling arm n in state s n is given by R n (s n ). There is a set N = {1, .., N} of availabl...
What is the multi-armed bandit problem? MAB is named after a thought experiment where a gambler has to choose among multiple slot machines with different payouts, and a gambler’s task is to maximize the amount of money he takes back home. Imagine for a moment that you’re the gambler. ...
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 ...
Epsilon-Greedy supposed an k arm(slot) and set ε a little number between [0,0.1] In short, epsilon-greedy means pick the current best option ("greedy") most of the time---(1-ε) + ε/k but pick a random option with a small probability sometimes for other option---(k-1)ε/k ...