Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
arXiv:2605.30461v1 Announce Type: new Abstract: We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual variables. Our method targets systems where agents have separable dynamics but must coordinate to satisfy global resource constraints, a setting in which, as we demonstrate empirically, independent learning fails to produce feasible solutions because agents cannot determine appropriate individual contributions toward collective constraint satisfaction. The key technical cont
The increasing complexity of AI systems and the demand for robust multi-agent coordination in real-world constrained environments necessitate novel distributed learning approaches.
This research provides a foundational step towards more reliable and scalable autonomous multi-agent systems, particularly relevant for applications requiring coordinated resource management and constraint satisfaction.
The ability to develop more robust and scalable multi-agent AI systems capable of operating under complex, interconnected constraints is enhanced, moving beyond independent learning paradigms.
- · Logistics and supply chain management
- · Autonomous robotics and drone fleets
- · Smart grid and resource allocation systems
- · AI agents developers
- · Systems relying on centralized control for multi-agent coordination
- · Inefficient independent learning approaches for constrained environments
Improved coordination and efficiency in distributed AI systems operating under shared constraints.
Accelerated deployment of autonomous agent swarms in applications like smart cities, warehousing, and defense.
Enhanced operational resilience and scalability of AI-powered critical infrastructure, reducing human oversight requirements.
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