Online reinforcement learning for multimedia buffer control

We formulate the multimedia buffer control problem as a Markov decision process. Because the application´s rate-distortion-complexity behavior is unknown a priori, the optimal buffer control policy must be learned online. To this end, we adopt a low complexity reinforcement learning algorithm called Q-learning to learn the optimal control policy at run-time. We propose an accelerated Q-learning algorithm that exploits partial knowledge about the system´s dynamics in order to dramatically improve the performance. In our experiments, we show that the proposed application-aware reinforcement learning algorithm performs significantly better than existing application-independent reinforcement learning algorithms.