Symbolic Reinforcement Learning for Safe RAN Control
This work addresses safety in cellular network control for network operators, but it appears incremental as it combines existing techniques like RL and model-checking without introducing a fundamentally new approach.
The paper tackles the problem of ensuring safe control in Radio Access Networks by proposing a Symbolic Reinforcement Learning architecture that uses Linear Temporal Logic specifications and model-checking to shield RL agents, resulting in optimized network performance as measured by Key Performance Indicators.
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.