# Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems

arXiv: https://arxiv.org/abs/2510.21427 · HTML: https://mlsanity.com/paper/2510.21427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 4 of 4)

- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Invariant Causal Imitation Learning for Generalizable Policies](https://mlsanity.com/paper/2311.01489/) (2023)
- [Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation](https://mlsanity.com/paper/2306.02747/) (2023)
- [Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication](https://mlsanity.com/paper/2307.12287/) (2023)

## Abstract (arXiv)

Large-scale networked systems, such as traffic, power, and wireless grids, challenge reinforcement-learning agents with both scale and environment shifts. To address these challenges, we propose GSAC (Generalizable and Scalable Actor-Critic), 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 causal recovery, actor-critic convergence, and adaptation gap, and show that GSAC adapts rapidly and significantly outperforms learning-from-scratch and conventional adaptation baselines.