  RF Network parametric optimization requires a wealth of experience and
knowledge to achieve the optimal balance between coverage, capacity, system
efficiency and customer experience from the telecom sites serving the users.
With 5G, the complications of Air interface scheduling have increased due to
the usage of massive MIMO, beamforming and introduction of higher modulation
schemes with varying numerologies. In this work, we tune a machine learning
model to "learn" the best combination of parameters for a given traffic profile
using Cross Entropy Method Reinforcement Learning and compare these with RF
Subject Matter Expert "SME" recommendations. This work is aimed towards
automatic parameter tuning and feature optimization by acting as a Self
Organizing Network module
