Techniques are described herein for using an intelligent Reinforcement Learning (RL) based model (agent) along with the respective intelligent and adaptive environment to analyze and learn historical monitoring data collected from on-premise collaboration deployments. The usage pattern may be accurately predicted for each specific day of the week as well as for the next day of the week in corresponding collaboration deployments. Based on the prediction, the administrator may be notified of the predicted network requirement for the next specific day(s). Communication system configuration can also be regulated intelligently as per the requirement for the day(s). If required, the output of the prediction system can be fed to the network control layer or communication system configuration management layer to automatically tune the system as per the predicted demand.
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Mukherjee, Anupam and Chidambaram, Rajarathinam, "AUTOMATED INTELLIGENT COMMUNICATION SYSTEM CONFIGURATION REGULATOR USING REINFORCEMENT LEARNING ALGORITHM", Technical Disclosure Commons, (June 25, 2020)