提出动态训练策略，在边缘计算中平衡学习成本与效率。
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
- 根据资源开销动态决定是否训练DRL代理，避免盲目学习
- 在真实训练开销下逼近理想学习性能，延迟降低23%
- 适用于任何存在训练代价的智能决策场景，如移动边缘计算
人工智能是6G网络的关键组件，可使通信与计算服务适应终端用户的需求和行为模式。移动边缘计算（MEC）的资源管理是人工智能应用的典型例子：网络边缘可用的计算资源需合理分配给不同优先级、不同延迟要求的用户任务。尽管研究社区已开发出多种AI算法实现资源分配，但普遍忽视了一个关键问题——学习本身是计算密集型任务，忽略学习成本会导致仿真环境过于理想化。本文考虑更现实的情形，明确将学习成本纳入考量，提出一种新算法，用于动态决定何时训练用于资源分配的深度强化学习（DRL）代理。该方法具有高度通用性，可直接应用于任何存在训练开销的场景，并能在真实训练条件下逼近理想学习代理的性能。
原文摘要 · Abstract (English)
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.
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