Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on the action and context. We consider this problem under a realizability assumption: there exists a function in a (known...
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
Li, L., Chu, W., Langford, J., and Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In WWW. Li, Y.Reinforcement Learning Applications. ArXiv, 2019.
▲图1. Reinforcement Learning Framework Bandit算法是一类用来实现Exploitation-Exploration机制的策略。根据是否考虑上下文特征,Bandit算法分为context-free bandit和contextual bandit两大类。接下来我们即将介绍考虑上下文特征的一种在线学习算法-LinUCB,我们在计算参数及最后推荐结果的时候,用到以下几部分的信息:上下文特征 x...
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...
With advancements in machine learning techniques and algorithms, contextual bandits have become more robust and efficient. Approaches like Thompson Sampling, Upper Confidence Bounds, and Gradient-based methods have proven effective in balancing exploration and exploitation in contextual bandit settings. In co...
We introduce a new family of margin-based regret guarantees for adversarial contextual bandit learning. Our results are based on multiclass surrogate losses. Using the ramp loss, we derive a universal margin-based regret bound in terms of the sequential metric entrop...
LinUCB是处理Contextual Bandit的一个方法,在LinUCB中,设定每个arm的期望收益为该arm的特征向量(context)的线性函数,如下: LinUCB与相对于传统的在线学习(online learning)模型(比如ftrl)相比,主要有2点区别: 每个arm学习一个独立的模型(context只需要包含user-side和user-arm interaction的特征,不需要包含arm-side特征)...
Contextual Bandit Learning-Based Viewport Prediction for 360 Videodoi:10.1109/VR.2019.8797830Joris HeyseFilip De TurckMaria Torres VegaFemke De BackereIEEE
In accordance, we propose a contextual bandit al- gorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in ...