  Bandwidth estimation and congestion control for real-time communications
(i.e., audio and video conferencing) remains a difficult problem, despite many
years of research. Achieving high quality of experience (QoE) for end users
requires continual updates due to changing network architectures and
technologies. In this paper, we apply reinforcement learning for the first time
to the problem of real-time communications (RTC), where we seek to optimize
user-perceived quality. We present initial proof-of-concept results, where we
learn an agent to control sending rate in an RTC system, evaluating using both
network simulation and real Internet video calls. We discuss the challenges we
observed, particularly in designing realistic reward functions that reflect
QoE, and in bridging the gap between the training environment and real-world
networks.
