Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
Hao Liang, shuqing shi, Yudi Zhang, Biwei Huang, Yali Du
Large‑scale networked systems—traffic, power, and wireless grids—challenge reinforcement‑learning agents with both scale and environment shifts. To address these challenges, we propose \texttt{GSAC} (\textbf{G}eneralizable and \textbf{S}calable \textbf{A}ctor‑\textbf{C}ritic), a framework that couples  causal representation learning with meta actor‑critic learning to achieve both scalability and domain generalization. Each agent first learns a sparse local causal mask that provably identifies the minimal neighborhood variables influencing its dynamics, yielding exponentially tight approximately compact representations (ACRs) of state and domain factors. These ACRs bound the error of truncating value functions to $\kappa$-hop neighborhoods, enabling efficient learning on graphs. A meta actor‑critic then trains a shared policy across multiple source domains while conditioning on the compact domain factors; at test time, a few trajectories suffice to estimate the new domain factor and deploy the adapted policy. We establish finite‑sample guarantees on casual recovery, actor-critic convergence, and adaptation gap, and show on wireless‑communication benchmarks that \texttt{GSAC} adapts rapidly and decisively outperforms training from scratch.