An improved upper bound on the expected regret of UCB-type policies for a matching-selection bandit problem

Operations Research Letters, Volume 43, Issue 6, 2015, Pages 558-563.

Cited by: 2|Views7
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Abstract:

We improved an upper bound on the expected regret of a UCB-type policy LLR for a bandit problem that repeats the following rounds: a player selects a maximal matching on a complete bipartite graph K M , N and receives a reward for each component edge of the selected matching. Rewards are assumed to be generated independently of its previo...More

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