Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
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We develop the first general semi-bandit algorithm that simultaneously achieves O( T) regret for stochastic environments and O(√(T)) regret for adversarial environments without knowledge of the regime or the number of rounds T. The leading problem-dependent constants of our bounds are not only optimal in some worst-case sense studied previously, but also optimal for two concrete instances of semi-bandit problems. Our algorithm and analysis extend the recent work of (Zimmert & Seldin, 2019) for the special case of multi-armed bandit, but importantly requires a novel hybrid regularizer designed specifically for semi-bandit. Experimental results on synthetic data show that our algorithm indeed performs well uniformly over different environments. We finally provide a preliminary extension of our results to the full bandit feedback.
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