The Sample Complexity of Exploration in the Multi-Armed Bandit Problem

@inproceedings{Mannor2003TheSC,
  title={The Sample Complexity of Exploration in the Multi-Armed Bandit Problem},
  author={Shie Mannor and John N. Tsitsiklis},
  booktitle={Journal of Machine Learning Research},
  year={2003}
}
We consider the multi-armed bandit problem under the PAC (“probably approximately correct”) model. It was shown by Even-Dar et al. (2002) that given n arms, a total of O ( (n/ε2) log(1/δ) ) trials suffices in order to find an ε-optimal arm with probability at least 1 − δ. We establish a matching lower bound on the expected number of trials under any sampling policy. We furthermore generalize the lower bound, and show an explicit dependence on the (unknown) statistics of the arms. We also… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 14 references

Sequential analysis: Tests and Confidence Intervals

  • D. Siegmund
  • 1985
Highly Influential
4 Excerpts

Asymptotically optimal procedures for sequential adaptive selection of the best of several normal means

  • C. Jennison, I. M. Johnstone, B. W. Turnbull
  • Statistical decision theory and related topics…
  • 1982
Highly Influential
4 Excerpts

Lower bounds on the sample complexity of exploration in the multiarmed bandit problem

  • S. Mannor, J. N. Tsitsiklis
  • Sixteenth Annual Conference on Computational…
  • 2003
1 Excerpt

A dynamic allocation index for the sequential design of experiments

  • J. Gani, K. Sarkadi, I. Vincze
  • 2002

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