Few-Shot Meta-Learned Safety Shields for Real-Time Adaptive Optimization in Sustainable Smart City Digital Twins

Abstract

This paper presents a meta-learned constrained reinforcement learning orchestrator that replaces traditional static mixed-integer linear programming solvers for sustainable urban resource management. The conventional approach to coordinating water, energy, and waste systems across heterogeneous districts suffers from rigid constraint formulations that cannot adapt to rapidly changing environmental conditions and demand patterns. Our core innovation is a task-conditioned constraint module that dynamically reformulates sustainability boundaries—such as minimum water pressure or maximum transformer load—based on real-time telemetry, surrogate model outputs, and spectral entropy of consumption signals. These adaptive constraints are integrated into the reinforcement learning objective through a Lagrangian barrier formulation, which a PID controller dynamically weights. Furthermore, we introduce a differentiable physics-informed neural network that continuously predicts resource state evolution and outputs a violation risk field thirty seconds into the future. This risk field then serves as a corrective shielding signal during a model-agnostic meta-learning outer loop, which fine-tunes the policy parameters every five minutes using only sixty-four recent transitions from the target district. The meta-controller executes asynchronously on cloud GPUs and pushes updated policies to edge nodes via gRPC, thereby enabling sub-second policy adaptation without interrupting real-time control. We demonstrate that this architecture achieves few-shot adaptation across multiple urban districts without retraining from scratch, substantially reducing constraint violations during distribution shifts compared to a fixed-solver baseline.