Improved Multiplayer Bandit Algorithm for Bernoulli Rewards
We study the multiplayer multi-armed bandit problem with information asymmetry under Bernoulli rewards, for three information structures: asymmetry in actions, in rewards, and in both. Replacing the Hoeffding-style confidence intervals of prior work with Kullback--Leibler (KL) divergence-based bounds gives strictly tig...
K. Nguyen, Ricardo Parada, William Chang
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