MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), safety filtering, or Model Predictive Control (MPC) either lack strict safety guarantees, suffer from conservatism, or fail to scale effectively. We propose MAD-PINN, a decentralized physics-informed machine learning framework for solving the multi-agent state-constrained optimal control problem (MASC-OCP). Our method leverages an epigraph-based reformulation of
The increasing complexity and scale of multi-agent systems, particularly in robotics and autonomous applications, necessitate more robust solutions for safety and optimality that current methods fail to provide.
This development offers a pathway to highly scalable and provably safe multi-agent AI systems, addressing a critical bottleneck in deploying advanced autonomous technologies.
The ability to co-optimize safety and performance in decentralized multi-agent systems with stronger guarantees changes the feasibility and deployment timelines for complex robotic and autonomous applications.
- · Robotics manufacturers
- · Logistics and supply chain
- · Defense contractors
- · AI software developers
- · Companies relying on centralized control systems
- · Inefficient multi-agent system developers
Improved safety and efficiency in autonomous systems accelerate their adoption across various industries.
The proliferation of safe, decentralized AI agents could lead to new forms of automated infrastructure and services.
Enhanced multi-agent control capabilities may influence geopolitical power dynamics through advanced military and economic autonomy.
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Read at arXiv cs.AI