Towards Effective Reinforcement Learning in Video Conferencing using Network Status Data and Model Analysis

Many studies are applying reinforcement learning to real-world problems. However, this is a difficult problem, and its application in the real world requires solving many challenges. Therefore, in order to solve this problem well, it is necessary to understand the process of data collection in the real world and the data collected through this process. Video conferencing is also an example of the application of reinforcement learning in the real world, so it is necessary to understand the video conferencing system used and the data collected through it. To this end, this paper presents a process to collect network status information data from a video conferencing system and introduces data and model analysis methods for effective reinforcement learning environment settings and problem definition. In addition, among various problems in video conferencing, the video quality selection problem is set as the target problem, and we trained the model to perform the introduced feature importance analysis. We also included the performance evaluation results and correlation with data analysis results.