arXiv:2602.16966v2 Announce Type: replace Abstract: Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-local, meaning a perturbation at one agent affects the long-run value at another agent weakly when the two are far apart. In the average-reward setting, the standard way to certify locality is the Dobrushin row-sum bound on a single matrix $C^\pi$ that captures how each agent's next state depends on each other agent's current state. To make this matrix easy to work w
Rapid progress in AI research, particularly in multi-agent systems, necessitates more robust theoretical frameworks for scalable and decentralized applications. This paper addresses a core challenge in making such systems practical.
Advanced multi-agent reinforcement learning (MARL) is crucial for complex autonomous systems, requiring solutions for decentralized operation and decision-making without global information. This framework improves the theoretical foundation for such solutions.
The ability to certify locality in MARL systems through a unified framework improves the reliability and scalability of decentralized AI, making them more applicable to real-world scenarios. It simplifies the design of agent interactions.
- · AI researchers
- · Robotics industry
- · Autonomous systems developers
- · Centralized control system paradigms
- · Less scalable MARL approaches
Improved design and deployment of large-scale multi-agent AI systems in various real-world applications.
Accelerated development of autonomous fleets, swarm robotics, and complex industrial automation.
Enhanced resilience and efficiency in critical infrastructure managed by decentralized AI agents, reducing single points of failure.
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Read at arXiv cs.LG