In the past few years, Deep Reinforcement Learning (DRL) has become a
valuable solution to automatically learn efficient resource management
strategies in complex networks. In many scenarios, the learning task is
performed in the Cloud, while experience samples are generated directly by edge
nodes or users. Therefore, the learning task involves some data exchange which,
in turn, subtracts a certain amount of transmission resources from the system.
This creates a friction between the need to speed up convergence towards an
effective strategy, which requires the allocation of resources to transmit
learning samples, and the need to maximize the amount of resources used for
data plane communication, maximizing users' Quality of Service (QoS), which
requires the learning process to be efficient, i.e., minimize its overhead. In
this paper, we investigate this trade-off and propose a dynamic balancing
strategy between the learning and data planes, which allows the centralized
learning agent to quickly converge to an efficient resource allocation strategy
while minimizing the impact on QoS. Simulation results show that the proposed
method outperforms static allocation methods, converging to the optimal policy
(i.e., maximum efficacy and minimum overhead of the learning plane) in the long
run.

本文介绍了动态平衡策略，旨在在云端和边缘节点之间进行数据交换并学习最有效的资源管理策略，以最大程度地减少学习过程的开销，同时尽可能地提高用户的服务质量。