Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is a meaningful example of AI application: computational resources available at the network edge need to be carefully allocated to users, whose jobs may have different priorities and latency requirements. The research community has developed several AI algorithms to perform this resource allocation, but it has neglected a key aspect: learning is itself a computationally demanding task, and considering free training results in idealized conditions and performance in simulations. In this work, we consider a more realistic case in which the cost of learning is specifically accounted for, presenting a new algorithm to dynamically select when to train a Deep Reinforcement Learning (DRL) agent that allocates resources. Our method is highly general, as it can be directly applied to any scenario involving a training overhead, and it can approach the same performance as an ideal learning agent even under realistic training conditions.

翻译：人工智能（AI）是6G网络的关键组成部分，它使通信与计算服务能够适应终端用户的需求和流量模式。移动边缘计算（MEC）的管理是AI应用的一个典型案例：网络边缘可用的计算资源需要被谨慎地分配给用户，因为用户任务可能具有不同的优先级和延迟要求。研究界已开发了多种AI算法来执行此类资源分配，但忽略了一个关键方面：学习本身是一项计算密集型任务，而假设训练无成本会导致仿真条件下的理想化结果与性能。在本工作中，我们考虑了一个更现实的场景，其中明确计入了学习成本，并提出了一种新算法来动态选择何时训练执行资源分配的深度强化学习（DRL）智能体。我们的方法具有高度通用性，可直接应用于任何涉及训练开销的场景，并且即使在现实训练条件下也能逼近理想学习智能体的性能。