Online convex optimization has been extensively studied in the recent learning literature. In this ongoing theoretical work, we extend the framework to consider similarly formulated online average cost constraints.
Cellular channels are increasingly used for sensitive real-time applications. For example, real time video can now be broadcast over parallel cellular channel, possibly from a moving vehicle. Such channels are characterized by high variability, and require improved flow control algorithms to maintain stable flow. This work addresses the application of deep learning algorithms to develop suitable flow control and scheduling algorithm under real-time delay constraints.
Burstiness Constraints characterize various dynamic processes, such as traffic demand in communication networks. We consider the optimal control of MDPs subject to such constraints, providing the theoretical framework and effective algorithms for this problem.
