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In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is named from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to…
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bandit problem reward multi-armed displaystyle bandits algorithm arm time strategies also probability expected exploration optimal exploitation rewards problems learning contextual
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Multi-armed bandit | is a | contextual multi-armed bandit | 0.90 | text |
| managing research projects in a large organization | instance of | multi-armed bandits have been used to model problems | 0.80 | text |
| like a science foundation or a pharmaceutical company | instance of | multi-armed bandits have been used to model problems | 0.80 | text |
| crowdsourcing | instance of | there is usually a cost associated with the resource consumed by each action and the total cost is limited by a budget in many applications | 0.80 | text |
| clinical trials | instance of | there is usually a cost associated with the resource consumed by each action and the total cost is limited by a budget in many applications | 0.80 | text |
| UCB won't be able to react very quickly to these changes | instance of | etc. then algorithms | 0.80 | text |
| Multi-armed bandit | related to Adversarial bandit | Another | 0.60 | section |
| Multi-armed bandit | related to Adversarial bandit | Auer | 0.60 | section |
| Multi-armed bandit | related to Adversarial bandit | Cesa-Bianchi | 0.60 | section |
| Multi-armed bandit | related to Adversarial bandit | In | 0.60 | section |
| Multi-armed bandit | related to Adversarial bandit | This | 0.60 | section |
| Multi-armed bandit | related to Best arm identification | An | 0.60 | section |
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