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.

翻译：在本文中,我们展示了用于无线电接入网络应用安全控制的一个符号强化学习(SRL)架构。在我们的自动化工具中,用户可以选择用线性时空逻辑(LTL)表达的高级安全规格来屏蔽在特定蜂窝网络运行的RL代理,目的是通过某些关键业绩指标(KPIs)进行衡量,优化网络性能。在拟议架构中,网络安全屏蔽是通过对通过强化学习提取的综合离散系统模型(automata)进行模式检查的技术来确保的。我们展示用户界面(UI)帮助用户设定结构的意向规格并检查允许和阻断行动的差异。