Two share links about bandit algorithm

博客分享了多臂老虎机算法相关内容,介绍了文章发布链接,还提出什么是多臂老虎机算法,指出一个臂代表一个选项。

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此文同时在 http://kunth.github.io/2014/04/23/bandit-algorithm.html


Two share links about bandit algorithm

what's (multi-arm) bandit algorithm ?

epsilon-greedy algorithm 

With probability 1 – epsilon, the epsilon-Greedy algorithm exploits the best known option. 
• With probability epsilon / 2, the epsilon-Greedy algorithm explores the best known option. 
• With probability epsilon / 2, the epsilon-Greedy algorithm explores the worst known option. 

one arm denotes one option

when pulled, any given arm will output a reward. 
you need to cope with risk by figuring out which arm has the highest average reward. 
what makes a bandit problem special is that we only receive a small amount of the information about the rewards from each arm. 
we only find out about the reward that was given out by the arm we actually pulled. 
whichever arm we pull, we miss out on information about hte other arms that we didn't pull. 
every time we experiment with an arm that isn't the best arm, we lose reawrd because we could, at least in principle, have pulled on a better arm. 

Here are two links about multi-bandit algorithm that may help you 
Bndit algorithm for website optimization
Algorithms for the multi-armed bandit problem


### 鲁棒性Bandit算法中的不良臂处理 在强化学习环境中,当面对不确定性和潜在的异常情况时,鲁棒性Bandit算法能够有效应对这些挑战。对于不良臂(bad arms),即那些可能带来负面效果的选择,这类算法提供了多种机制来识别并减少其影响。 一种常见的策略是在决策过程中引入探索与利用之间的平衡。通过增加对未知选项的好奇心奖励,可以促使模型更频繁地测试不同手臂的表现,从而更快发现哪些是真正表现不佳的手臂[^1]。这种方法不仅有助于避免长期陷入次优解,还能增强系统的适应能力。 为了进一步提高对抗不良事件的能力,某些研究提出了基于上下文信息的方法。例如,在线学习框架下,可以通过观察环境特征和其他相关变量的变化趋势,动态调整各手臂的概率分布。这使得即使某个特定时期内出现了几个连续失败的结果,只要能从中提取到有用模式,则后续仍有机会纠正错误估计。 此外,还存在专门针对极端情况下设计的技术——如安全约束下的多臂赌博机问题解决方案。此类方法旨在确保在整个实验周期内的任何时刻都不会违反预设的安全界限;一旦检测到有违反风险的行为即将发生,则立即采取行动阻止,并重新评估当前最优路径。 ```python import numpy as np class RobustBanditAlgorithm: def __init__(self, num_arms): self.num_arms = num_arms self.counts = [0] * num_arms self.values = [0.0] * num_arms def select_arm(self): if min(self.counts) == 0: return self.counts.index(min(self.counts)) ucb_values = [ value + np.sqrt(2 * np.log(sum(self.counts)) / count) for value, count in zip(self.values, self.counts)] return ucb_values.index(max(ucb_values)) def update(self, chosen_arm, reward): n = self.counts[chosen_arm] value = self.values[chosen_arm] new_value = ((n * value) + reward) / (n + 1) self.values[chosen_arm] = new_value self.counts[chosen_arm] += 1 # Implement safety checks here to handle 'bad' arms and provide recourse. ```
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