  Non-differentiable controllers and rule-based policies are widely used for
controlling real systems such as telecommunication networks and robots.
Specifically, parameters of mobile network base station antennas can be
dynamically configured by these policies to improve users coverage and quality
of service. Motivated by the antenna tilt control problem, we introduce
Model-Based Residual Policy Learning (MBRPL), a practical reinforcement
learning (RL) method. MBRPL enhances existing policies through a model-based
approach, leading to improved sample efficiency and a decreased number of
interactions with the actual environment when compared to off-the-shelf RL
methods.To the best of our knowledge, this is the first paper that examines a
model-based approach for antenna control. Experimental results reveal that our
method delivers strong initial performance while improving sample efficiency
over previous RL methods, which is one step towards deploying these algorithms
in real networks.
