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New protocol tackles adversarial multiplayer bandits with limited communication

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new protocol for adversarial multiplayer bandits, specifically addressing scenarios with multiple players, limited communication, and no shared randomness. The proposed method uses a Monte Carlo public constructor to establish a common learning schedule and synchronize players before learning begins. This protocol ensures that regret is limited even during periods with minimal feedback, maintaining valid reward estimates while exchanging assignments and scores. AI

IMPACT Introduces a novel protocol for multi-agent learning in bandit settings, potentially improving coordination and efficiency in decentralized systems.

RANK_REASON Academic paper detailing a new protocol for a specific type of bandit problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

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

New protocol tackles adversarial multiplayer bandits with limited communication

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Academic paper detailing a new protocol for a specific type of bandit problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Chenyu Gan ·

    Square-Root Regret for Adversarial Multiplayer Bandits without Collision Information or Shared Randomness

    We study adversarial multiplayer bandits with $K$ arms and $2\le m<K$ labeled players, without collision information, shared randomness, or an external communication channel. We design a constructive communication and synchronization protocol with a Monte Carlo public constructor…

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