Introduces a pseudo-metric to quantify advice usefulness and shows reliable advice enables efficient approximate Stackelberg strategies while unreliable advice blocks simultaneous near-Stackelberg and no-regret guarantees but permits weak dominance in some correlated equilibria.
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Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Introduces a pseudo-metric to quantify advice usefulness and shows reliable advice enables efficient approximate Stackelberg strategies while unreliable advice blocks simultaneous near-Stackelberg and no-regret guarantees but permits weak dominance in some correlated equilibria.