Fast Context Adaptation in Cost-Aware Continual Learning
Seyyidahmed Lahmer Federico Mason Federico Chiariotti Andrea Zanella
In the past few years, DRL has become a valuable solution to automatically
learn efficient resource management strategies in complex networks with
time-varying statistics. However, the increased complexity of 5G and Beyond
networks requires correspondingly more complex learning agents and the learning
process itself might end up competing with users for communication and
computational resources. This creates friction: on the one hand, the learning
process needs resources to quickly convergence to an effective strategy; on the
other hand, the learning process needs to be efficient, i.e., take as few
resources as possible from the user's data plane, so as not to throttle users'
QoS. In this paper, we investigate this trade-off and propose a dynamic
strategy to balance the resources assigned to the data plane and those reserved
for learning. With the proposed approach, a learning agent can quickly converge
to an efficient resource allocation strategy and adapt to changes in the
environment as for the CL paradigm, while minimizing the impact on the users'
QoS. Simulation results show that the proposed method outperforms static
allocation methods with minimal learning overhead, almost reaching the
performance of an ideal out-of-band CL solution.