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[2602.16965] Multi-Agent Lipschitz Bandits
Summary
The paper presents a novel approach to the multi-agent Lipschitz bandit problem, proposing a communication-free policy that maximizes collective rewards while minimizing coordination costs.
Why It Matters
This research addresses a significant challenge in decentralized multi-agent systems, where effective coordination is crucial for maximizing rewards. By establishing a near-optimal regret bound, the findings could enhance strategies in various applications, including robotics and AI agents, where collaboration is essential.
Key Takeaways
- Introduces a modular protocol for multi-agent coordination in bandit problems.
- Achieves a near-optimal regret bound, improving efficiency in decision-making.
- Extends the framework to general distance-threshold collision models.
Computer Science > Machine Learning arXiv:2602.16965 (cs) [Submitted on 18 Feb 2026] Title:Multi-Agent Lipschitz Bandits Authors:Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen View a PDF of the paper titled Multi-Agent Lipschitz Bandits, by Sourav Chakraborty and 3 other authors View PDF HTML (experimental) Abstract:We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward. Our objective is to design a communication-free policy that maximizes collective reward, with coordination costs that are independent of the time horizon $T$. We propose a modular protocol that first solves the multi-agent coordination problem -- identifying and seating players on distinct high-value regions via a novel maxima-directed search -- and then decouples the problem into $N$ independent single-player Lipschitz bandits. We establish a near-optimal regret bound of $\tilde{O}(T^{(d+1)/(d+2)})$ plus a $T$-independent coordination cost, matching the single-player rate. To our knowledge, this is the first framework providing such guarantees, and it extends to general distance-threshold collision models. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.16965 [cs.LG] (or arXiv:2602.16965v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.16965 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Amit Kiran Rege [vie...
