Symbolic Reinforcement Learning for Safe RAN Control
Abstract
In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL) architecture for safe control in Radio Access Network (RAN) applications. In our automated tool, a user can select a high-level safety specifications expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a given cellular network with aim of optimizing network performance, as measured through certain Key Performance Indicators (KPIs). In the proposed architecture, network safety shielding is ensured through model-checking techniques over combined discrete system models (automata) that are abstracted through reinforcement learning. We demonstrate the user interface (UI) helping the user set intent specifications to the architecture and inspect the difference in allowed and blocked actions.
Cite
@article{arxiv.2103.06602,
title = {Symbolic Reinforcement Learning for Safe RAN Control},
author = {Alexandros Nikou and Anusha Mujumdar and Marin Orlic and Aneta Vulgarakis Feljan},
journal= {arXiv preprint arXiv:2103.06602},
year = {2021}
}
Comments
The paper has been accepted to be presented in 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), May 3-7, London, UK (demo track)