  Safe interaction with the environment is one of the most challenging aspects
of Reinforcement Learning (RL) when applied to real-world problems. This is
particularly important when unsafe actions have a high or irreversible negative
impact on the environment. In the context of network management operations,
Remote Electrical Tilt (RET) optimisation is a safety-critical application in
which exploratory modifications of antenna tilt angles of base stations can
cause significant performance degradation in the network. In this paper, we
propose a modular Safe Reinforcement Learning (SRL) architecture which is then
used to address the RET optimisation in cellular networks. In this approach, a
safety shield continuously benchmarks the performance of RL agents against safe
baselines, and determines safe antenna tilt updates to be performed on the
network. Our results demonstrate improved performance of the SRL agent over the
baseline while ensuring the safety of the performed actions.
