Social Learning in Multi Agent Multi Armed Bandits
نویسندگان
چکیده
منابع مشابه
Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits
In this paper, we introduce a multi-agent multi-armed bandit-based model for ad hoc teamwork with expensive communication. The goal of the team is to maximize the total reward gained from pulling arms of a bandit over a number of epochs. In each epoch, each agent decides whether to pull an arm and hence collect a reward, or to broadcast the reward it obtained in the previous epoch to the team a...
متن کاملActive Learning in Multi-armed Bandits
We consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The decision maker can select which option to generate the next sample from, the goal being to produce estimates with equally good precision for all the options. If sample means are used to estimate the unknown values then the optimal solution, assuming full knowledge...
متن کاملContextual Multi-Armed Bandits
We study contextual multi-armed bandit problems where the context comes from a metric space and the payoff satisfies a Lipschitz condition with respect to the metric. Abstractly, a contextual multi-armed bandit problem models a situation where, in a sequence of independent trials, an online algorithm chooses, based on a given context (side information), an action from a set of possible actions ...
متن کاملStaged Multi-armed Bandits
In conventional multi-armed bandits (MAB) and other reinforcement learning methods, the learner sequentially chooses actions and obtains a reward (which can be possibly missing, delayed or erroneous) after each taken action. This reward is then used by the learner to improve its future decisions. However, in numerous applications, ranging from personalized patient treatment to personalized web-...
متن کاملMortal Multi-Armed Bandits
We formulate and study a new variant of the k-armed bandit problem, motivated by e-commerce applications. In our model, arms have (stochastic) lifetime after which they expire. In this setting an algorithm needs to continuously explore new arms, in contrast to the standard k-armed bandit model in which arms are available indefinitely and exploration is reduced once an optimal arm is identified ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM SIGMETRICS Performance Evaluation Review
سال: 2020
ISSN: 0163-5999
DOI: 10.1145/3410048.3410065