A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation
This work addresses the critical problem of ensuring safety in real-world Reinforcement Learning applications, specifically for network management operations like antenna tilt optimization, where unsafe actions can cause significant performance degradation.
This paper proposes a modular Safe Reinforcement Learning (SRL) architecture to optimize Remote Electrical Tilt (RET) in cellular networks. The architecture uses a safety shield to benchmark RL agent performance against safe baselines, ensuring safe antenna tilt updates and demonstrating improved performance over the baseline.
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.