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...
多臂老虎机深受学术界的宠爱,被统计学,运筹学,电子工程,经济学,计算机科学等多个领域的研究者所关注。这一模型假设简单,容易进行深入的理论分析,且在实际应用中有着广泛的应用场景。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...
UCB就是把所有arm的置信上界全部计算出来,然后选取出来最大的那个。它的特点就是 对于未知或较少尝试的arm,尽管其均值可能很低,但是由于其不确定性会导致置信区间的上界较大,从而有较大的概率触发exploration 对于已经很熟悉的arm(尝试过较多次),更多的是触发exploitation机制:如果其均值很高,会获得更多的利用机会;反...
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 ...
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 ‘...
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 ...
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...
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 ...
The standard way to compare different multi-armed bandit algorithms is to compute a regret metric. Regret is the difference between the expected value of the system, assuming you know the best arm, and the actual value of the system in experiments. For example, suppose you played the three ...
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (fidelities) and observation accuracy. We study both the best arm identification with fixed confidence (BAI) ...