Multi-Armed Bandits with Betting

author: Alexandru Niculescu Mizil, IBM Thomas J. Watson Research Center
published: Aug. 26, 2009,   recorded: June 2009,   views: 222

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Description

We study an extension to the stochastic multiarmed bandit problem where the learner has a budget ofK “coins” it can use in each round. The learner can use the coins to play multiple arms in each round, having the option to “bet” multiple coins on an arm. At the end of the round, the arms generate a reward that is proportional to the amount of coins invested in them.

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