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This work proposes a simple reduction that converts dueling feedback into approximate gradients, enabling the use of standard first-order methods, and shows that regret guarantees transfer under this reduction.
This work proposes Decentralized Barrier Follow-the-Regularized-Leader (Dec-BFTRL), and evaluates each agent's played action against the average of all local objectives, with applications to online continuous diminishing-return (DR) submodular maximization.
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