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RF Network parametric optimization requires a wealth of experience andknowledge to achieve the optimal balance between coverage, capacity, systemefficiency and customer experience from the telecom sites serving the users.With 5G, the complications of Air interface scheduling have increased due tothe usage of massive MIMO, beamforming and introduction of higher modulationschemes with varying numerologies. In this work, we tune a machine learningmodel to "learn" the best combination of parameters for a given traffic profileusing Cross Entropy Method Reinforcement Learning and compare these with RFSubject Matter Expert "SME" recommendations. This work is aimed towardsautomatic parameter tuning and feature optimization by acting as a SelfOrganizing Network module

Application of Reinforcement Learning for 5G Scheduling Parameter Optimization