we are using three four-armed bandit. What this means is that each bandit has four arms that can be pulled. Each bandit has different success probabilities for each arm, and as such requires different actions to obtain the best
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in ...
bandit = ContextualBandit(T, n_arms, n_features, h, noise_std=noise_std) regrets = np.empty((n_sim, T)) for i in range(n_sim): bandit.reset_rewards() model = NeuralUCB(bandit, hidden_size=hidden_size, reg_factor=1.0, delta=0.1, confidence_scaling_factor=confidence_scaling_factor,...
论文名:Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests 会议:AAAI 2020 Tag(By myself):contextual-bandit; 强化学习; 摘要&简介 摘要: 研究了一个高度非平稳环境下的context-bandit问题,这种环境普遍存在于各种推荐系统中(由于用户兴趣会随着时间变化)。考虑了两种模型(解耦收益和...
论文分析了已有的Bandit算法,包括UCB、E-Greedy、Thompson Smapling,然后提出了LinUCB算法,LinUCB分为两种: 简单的线性不相交模型 disjoint LinUCB 混合相交的线性模型 hybrid LinUCB 概述 人生中有很多选择问题,当每天中午吃饭的时候,需要选择吃饭的餐馆,那么就面临一个选择,是选择熟悉的好吃的餐馆呢,还是冒风险选择一个...
论文的最大贡献在于,对于contextual bandit,假设回报模型是linear的时候,可以得到UCB的解析解。 作者经过推导给出来两种线性回报函数的算法,一种是arm之间不共享参数,另外一种是arm之间有共享参数。 linucb_disjoint_linear.jpg 这里给出不共享参数(disjoint linear model)的伪码。作者说明,在计算上,是可以有一个缓存机...
第四步,更新,因为执行完action 之后,我有一个user 的feedback,可以去更新model 的参数。关于Contextual Bandit 比较有名的算法,我这里列举了两篇,第一篇就是LinUCB (WWW,2010),第二篇是Thompson Sampling (ICML,2013) 下面就引入我们下面那个topic,如何在有限的探索资源下去做Contextual Bandit的决策问题。场景如...
Recently, researchers have started to model interactions between users and search engines as contextual bandit problems, and initial methods for learning in this setting have been devised. Our research focuses on two aspects: balancing exploration and exploitation and inferring preferences from implicit ...
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we ...
2.1 User Browsing Model(UBM) Aleksandr Chuklin [2]等人提出了UBM,认为点击率ctr既受到user对item的喜好影响,也受到user能浏览到item的概率影响。 3. UBM-LinUCB 相比于LinUCB,为适配推荐场景同时推荐多个item,本文提出的bandit算法在每次尝试 ,选择一个arm的子集合 ...