Abstract
Techniques herein provide a verifiable, automated solution for translating network policies across diverse vendor environments. The techniques utilize a novel Semantic-Behavioral Policy Equivalence (SBPE) Framework to mathematically guarantee identical policy behavior, complemented by reinforcement learning to facilitate intelligent, adaptive, and efficient operational execution. This significantly reduces complexity and risk in multi-vendor network management environments.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bansal, Priyanka; Anurag, Pattamsetti; Kashyap, Anirudh; and Vakkalanka, Meghana, "MULTI AGENT REINFORCEMENT LEARNING USING SWARM INTELLIGENCE TO IMPLEMENT NETWORK POLICY TRANSLATION", Technical Disclosure Commons, (September 25, 2025)
https://www.tdcommons.org/dpubs_series/8639