Symbolic Reinforcement Learning for Safe RAN Control
  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.
