The rapid proliferation of distributed energy resources (DERs) in modern power grids introduces unprecedented complexity into real-time dispatch scheduling, demanding solutions that can operate under high uncertainty, partial observability, and volatile renewable generation. Existing multi-agent reinforcement learning (MARL) approaches for grid dispatch often suffer from weak inter-agent coordination, sample inefficiency, and fragility under measurement noise, largely because they rely on either independent exploration or expensive hidden-state exchange. We propose QS-MADS , a quorum-sensing-driven adaptive multi-agent scheduling framework that combines a topology-aware graph attention encoder with a lightweight scalar QS signal. During centralised training and optional online adaptation, each agent emits one 32-bit QS criticality signal; the trainer aggregates these scalars into a population consensus that governs a differentiable adaptive perturbation module used only for policy-gradient exploration. At deployment, the stochastic perturbation is removed and each deterministic actor executes from its local k -hop graph embedding, avoiding pairwise hidden-state communication. On IEEE 118-bus, RTS-96, and the CIGRE medium-voltage microgrid benchmarks, QS-MADS achieves up to 25% higher cumulative dispatch reward, reduces voltage violation events by 31%, and converges in up to 40% fewer training episodes relative to the strongest baseline, while maintaining millisecond-level inference latency and robust performance under measurement noise.
Zeng et al. (Mon,) studied this question.