arXiv:2602.06404v2 Announce Type: replace Abstract: We study distributed adversarial bandits, where $N$ agents cooperate to minimize the global average loss while observing only their own local losses. We show that the minimax regret for this problem is $\tilde{\Theta}(\sqrt{(\rho^{-1/2}+K/N)T})$, where $T$ is the horizon, $K$ is the number of actions, and $\rho$ is the spectral gap of the communication matrix. Our algorithm, based on a novel black-box reduction to bandits with delayed feedback, requires agents to communicate only through gossip. It achieves an upper bound that significantly i
The paper tackles a fundamental theoretical challenge in distributed AI cooperation, crucial for the maturation of agentic systems.
This research provides a more efficient and robust framework for agents to cooperate under uncertainty, directly impacting the scalability and reliability of distributed AI applications.
The proposed black-box approach allows more complex, decentralized AI systems to achieve near-optimal performance with less communication overhead, improving their practical deployment.
- · AI developers
- · Robotics
- · Distributed computing platforms
- · Logistics automation
- · Centralized AI systems
Improved performance and scalability of multi-agent AI systems in adversarial environments.
Accelerated development of autonomous AI agents capable of complex, cooperative tasks.
Enhanced resilience of critical infrastructure managed by distributed AI, less susceptible to single points of failure or localized attacks.
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