Best of both worlds: Stochastic & adversarial best-arm identification

Abstract : We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial scenario. However, this type of strategy is suboptimal when the rewards are sampled stochastically. Therefore, we ask: Can we design a learner that performs optimally in both the stochastic and adversarial problems while not being aware of the nature of the rewards? First, we show that designing such a learner is impossible in general. In particular, to be robust to adversarial rewards, we can only guarantee optimal rates of error on a subset of the stochastic problems. We give a lower bound that characterizes the optimal rate in stochastic problems if the strategy is constrained to be robust to adversarial rewards. Finally, we design a simple parameter-free algorithm and show that its probability of error matches (up to log factors) the lower bound in stochastic problems, and it is also robust to adversarial ones.
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https://hal.inria.fr/hal-01808948
Contributor : Michal Valko <>
Submitted on : Wednesday, June 6, 2018 - 11:22:11 AM
Last modification on : Friday, June 28, 2019 - 3:01:15 PM
Long-term archiving on: Friday, September 7, 2018 - 12:50:32 PM

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  • HAL Id : hal-01808948, version 1

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Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon, Alan Malek, Michal Valko. Best of both worlds: Stochastic & adversarial best-arm identification. COLT 2018 - Conference on Learning Theory, Jul 2018, Stockholm, Sweden. ⟨hal-01808948v1⟩

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