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New research explores Lipschitz bandits in multi-agent and dueling settings

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore advanced bandit algorithms for complex scenarios. The first paper addresses cooperative multi-agent bandits in continuous action spaces where the Lipschitz constant is unknown, proposing an algorithm that estimates this constant and uses a discretization approach to achieve regret guarantees. The second paper introduces the first algorithm for stochastic dueling bandits over continuous action spaces with Lipschitz structure, focusing on comparative feedback and achieving a logarithmic space complexity. AI

IMPACT These papers advance theoretical understanding and algorithmic capabilities in reinforcement learning, potentially leading to more efficient decision-making in complex, uncertain environments.

RANK_REASON Two academic papers published on arXiv detailing new algorithms for bandit problems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research explores Lipschitz bandits in multi-agent and dueling settings

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Andy Wang, Charlton Shih, William Chang ·

    DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks

    arXiv:2608.11873v1 Announce Type: new Abstract: In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may observe multiple clicks per session, introducing new c…

  2. arXiv cs.AI TIER_1 English(EN) · Ricardo Parada, Chenzhang Zhao, William Chang ·

    Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits

    arXiv:2608.10526v1 Announce Type: cross Abstract: Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown. We consider three information structures: (A)~unobserved actions wit…

  3. arXiv cs.LG TIER_1 English(EN) · Mudit Sharma, Shweta Jain, Vaneet Aggarwal, Ganesh Ghalme ·

    Lipschitz Dueling Bandits over Continuous Action Spaces

    arXiv:2604.00523v2 Announce Type: replace Abstract: We study for the first time, stochastic dueling bandits over continuous action spaces with Lipschitz structure, where feedback is purely comparative. While dueling bandits and Lipschitz bandits have been studied separately, thei…

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