本文主要对近期学习的 Contextual-free Bandits 做了些总结,话不多说以下进入正文,先从背景说起。 Bandit系列算法用于解决多臂赌博机 (MAB,Multi-Armed Bandit) 问题,MAB问题描述: 一个赌徒,要去摇老虎机,走进赌场一看,一排老虎机,外表一模一样,但是每个老虎机吐钱的概 率可不一样,他不知道每个老虎机吐...
Contextual multi-armed banditssequential decision makingPython machine learning librariesContextual multi-armed bandit algorithms are an effective approach for online sequential decision-making problems. However, there are limited tools available to support their adoption in the community. To fill this gap,...
Efficient experimentation and the multi-armed bandit Contextual Bandits: LinUCB Optimism in the Face of Uncertainty: the UCB1 Algorithm 关于Contextual Bandit算法在生产环境的推荐系统中如何实现、基础实现、工程框架,以及特征设计时的陷阱,超参数选择等内容,请查阅最新的文章《(杨旭东:在生产环境的推荐系统中部...
[1] Li, Lihong, et al. "A contextual-bandit approach to personalized news article recommendation." Proceedings of the 19th international conference on World wide web. 2010. [2] J.LangfordandT.Zhang.Theepoch-greedyalgorithmforcontextual multi-armed bandits. In Advances in Neural Information Process...
We present Exponentiated Gradient LINUCB, an algorithm for con-textual multi-armed bandits. This algorithm uses Exponentiated Gradient to find the optimal exploration of the LINUCB. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The ...
We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with side information). Epoch-Greedy has the following prop-erties: 1. No knowledge of a time horizon T is necessary. 2. The regret incurred by Epoch-Greedy is controlled by a sample complexity ...
MABWiser: Parallelizable Contextual Multi-Armed Bandits MABWiser (IJAIT 2021, ICTAI 2019) is a research library written in Python for rapid prototyping of multi-armed bandit algorithms. It supports context-free, parametric and non-parametric contextual bandit models and provides built-in parallelization...
In autonomous robotic decision-making under uncertainty, the tradeoff between exploitation and exploration of available options must be considered. If secondary information associated with options can be utilized, such decision-making problems can often be formulated as contextual multi-armed bandits (CMABs...
machine-learningcranstatisticsreinforcement-learningsimulationevaluationexplorationexploitationbandit-learningreinforcementmulti-armed-banditsmulti-armed-banditbanditcontextual-banditscontextualcmabmulti-armedbandit-experimentscontextual-bandit-policiesoffline-bandit
Such a "sparring" approach was previously proposed for dueling bandits by Ailon et al. (2014), though without details, and not in the contextual setting. 6 CONTEXTUAL DUELING BANDITS We consider using the multi-armed bandit algorithm Exp4.P (Beygelzimer et al., 2011) for this purpose in ...