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New algorithm optimizes learning in games with arbitrary delays

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called Multi-Anchor Optimistic Online Mirror Descent (MAO-OMD) for learning in zero-sum games with delayed feedback. This single algorithm achieves optimal fast rates in self-play for smooth games and simultaneously provides minimax optimal guarantees against any opponent. The algorithm is particularly effective in smooth zero-sum games with unknown feedback delays, offering improved regret bounds compared to standard methods. AI

IMPACT Introduces a novel algorithm for optimizing learning in games with delayed feedback, potentially impacting AI research in strategic decision-making.

RANK_REASON The cluster contains a research paper detailing a new algorithm for game theory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm optimizes learning in games with arbitrary delays

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The cluster contains a research paper detailing a new algorithm for game theory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Emmanuel Esposito, Andrew Jacobsen, Hao Qiu, Alexander Ryabchenko, Mengxiao Zhang ·

    Optimal Learning in Games with Arbitrary Delays: Best-of-Both-Worlds for Self-Play and Adversarial Opponents

    arXiv:2611.07773v1 Announce Type: cross Abstract: We study learning in zero-sum games with delayed feedback. We introduce Multi-Anchor Optimistic Online Mirror Descent (MAO-OMD), a single algorithm which obtains optimal fast rates in self-play on smooth games, while simultaneousl…

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