Forced-Exploration Based Algorithms for Playing in Stochastic Linear Bandits

  title={Forced-Exploration Based Algorithms for Playing in Stochastic Linear Bandits},
  author={Yasin Abbasi-Yadkori and Csaba Szepesv{\'a}ri},
We study stochastic linear payoff bandit problems and give a simple, computationally efficient algorithm whose regret, under certain regularity assumptions on the action set, is O(d √ T ), where d is the dimensionality of the action space and T is the time-horizon. However, this result is problem dependent and not a minimax bound. We show that our algorithm is able to achieve lower regret bounds when we have sparsity in the problem. Our experimental results support our upper bound and show that… CONTINUE READING

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Forced-exploration based algorithms for playing in bandits with large action sets

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