Computer Science – Information Theory
Scientific paper
2005-01-22
Computer Science
Information Theory
16 pages, 3 figures. To be published in the IEEE Transactions on Automatic Control
Scientific paper
10.1109/TAC.2005.844079
An extension of the traditional two-armed bandit problem is considered, in which the decision maker has access to some side information before deciding which arm to pull. At each time t, before making a selection, the decision maker is able to observe a random variable X_t that provides some information on the rewards to be obtained. The focus is on finding uniformly good rules (that minimize the growth rate of the inferior sampling time) and on quantifying how much the additional information helps. Various settings are considered and for each setting, lower bounds on the achievable inferior sampling time are developed and asymptotically optimal adaptive schemes achieving these lower bounds are constructed.
Kulkarni Sanjeev R.
Poor Harold Vincent
Wang Chih-Chun
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