arXiv:2602.16965v2 Announce Type: replace 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, while separating coordination costs from learning costs. We propose a modular protocol that first solves the multi-agent coordination problem by identifying and seating players on distinct, high-value regions via a novel maxima-directed search and then decouples the problem into $N$ independent sin
The increasing complexity and scale of multi-agent AI systems, particularly in decentralized settings, necessitate robust coordination and learning mechanisms to unlock their potential.
This research provides a foundational solution for enabling effective and efficient decentralized multi-agent learning with continuous action spaces, which is critical for scalable AI applications.
The ability to design communication-free, robust policies for decentralized multi-agent systems reduces design complexity and improves performance in continuous state-action environments, especially where collisions are costly.
- · AI agents developers
- · Robotics
- · Decentralized AI platforms
- · Logistics and supply chain optimization
- · Centralized control systems
- · Inefficient multi-agent coordination approaches
Improved performance and scalability of AI systems operating in decentralized and dynamic environments.
Accelerated development of autonomous agentic systems that can operate with less oversight and more resilience.
New competitive advantages for organizations capable of deploying highly coordinated, communication-free agent swarms in complex operational settings.
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Read at arXiv cs.LG