Reinforcement Learning

Reinforcement learning provides a natural framework for wireless control problems where decisions unfold over time and affect future system behaviour. It is especially relevant when the network must balance competing objectives such as quality of service, latency, fairness, and energy consumption under uncertainty.

In practice, this includes learning policies for scheduling, handover, power control, cooling, and broader orchestration tasks. The research challenge is to make these methods stable, sample-efficient, safe, and adaptable enough to operate in real communication systems rather than only in simulation.