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Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits
[article]
2019
arXiv
pre-print
We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend. We derive an asymptotic regret lower bound for any uniformly efficient algorithm in our setting. We then study a variant of Thompson sampling for Bernoulli rewards and a variant of KL-UCB for both single-parameter exponential families and bounded, finitely supported rewards.
arXiv:1606.09388v3
fatcat:uek4qx7oavhc7lkx4hydfifajy