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» The Nonstochastic Multiarmed Bandit Problem
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CORR
2006
Springer
83views Education» more  CORR 2006»
15 years 1 months ago
How to Beat the Adaptive Multi-Armed Bandit
The multi-armed bandit is a concise model for the problem of iterated decision-making under uncertainty. In each round, a gambler must pull one of K arms of a slot machine, withou...
Varsha Dani, Thomas P. Hayes
CORR
2010
Springer
187views Education» more  CORR 2010»
15 years 1 months ago
Learning in A Changing World: Non-Bayesian Restless Multi-Armed Bandit
We consider the restless multi-armed bandit (RMAB) problem with unknown dynamics. In this problem, at each time, a player chooses K out of N (N > K) arms to play. The state of ...
Haoyang Liu, Keqin Liu, Qing Zhao
CORR
2010
Springer
152views Education» more  CORR 2010»
14 years 8 months ago
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards
In the classic multi-armed bandits problem, the goal is to have a policy for dynamically operating arms that each yield stochastic rewards with unknown means. The key metric of int...
Yi Gai, Bhaskar Krishnamachari, Rahul Jain
ALT
2008
Springer
15 years 10 months ago
Active Learning in Multi-armed Bandits
In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which opti...
András Antos, Varun Grover, Csaba Szepesv&a...
ICASSP
2010
IEEE
15 years 1 months ago
Distributed learning in cognitive radio networks: Multi-armed bandit with distributed multiple players
—We consider a cognitive radio network with distributed multiple secondary users, where each user independently searches for spectrum opportunities in multiple channels without e...
Keqin Liu, Qing Zhao