Hyper-Efficient Coalition Formation in Dynamic Stochastic Games via Constrained Variational Inference

**Abstract:** This paper introduces a novel approach to coalition formation in dynamic stochastic games, a pressing issue in areas like resource allocation, distributed robotics, and multi-agent systems. Existing solutions struggle with scalability and adaptability to changing game dynamics. We propose a framework leveraging Constrained Variational Inference (CVI) coupled with a hierarchical reinforcement learning (HRL) architecture to efficiently determine stable coalitions in large, dynamically evolving game scenarios. Our system demonstrates a significant improvement in coalition formation speed and stability compared to traditional negotiation protocols, offering valuable implications for real-time distributed decision-making. This work offers a clear path to immediate commercialization, particularly within the burgeoning market for automated resource management and robotic swarm coordination.