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The growing complexity and capacity demands for mobile networks necessitateinnovative techniques for optimizing resource usage. Meanwhile, recentbreakthroughs have brought Reinforcement Learning (RL) into the domain ofcontinuous control of real-world systems. As a step towards RL-based networkcontrol, this paper introduces a new framework for benchmarking the performanceof an RL agent in network environments simulated with ns-3. Within thisframework, we demonstrate that an RL agent without domain-specific knowledgecan learn how to efficiently adjust Radio Access Network (RAN) parameters tomatch offline optimization in static scenarios, while also adapting on the flyin dynamic scenarios, in order to improve the overall user experience. Ourproposed framework may serve as a foundation for further work in developingworkflows for designing RL-based RAN control algorithms.

FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters