3 papers
cs.AI2026
Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Zihao Guo, Jianing Zhao, Ling Li +3
Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints. Existing approaches face a fundamental trade-…
cs.LG2026
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li +7
Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion…
cs.LG2025
Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
Hao Liang, Shuqing Shi, Yudi Zhang +2
Large-scale networked systems, such as traffic, power, and wireless grids, challenge reinforcement-learning agents with both scale and environment shifts. To address these challeng…