With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parallel to complexity. For some of the key applications targeted by future wireless, such as the medical field, strict and reliable performance guarantees are essential, but vanilla machine learning methods have been shown to struggle with these types of requirements. Therefore, the question is raised whether these methods can be extended to better deal with the demands imposed by such applications. In this paper, we look at a combinatorial resource allocation challenge with rare, significant events which must be handled properly. We propose to treat this as a multi-task learning problem, select two methods from this domain, Elastic Weight Consolidation and Gradient Episodic Memory, and integrate them into a vanilla actor-critic scheduler. We compare their performance in dealing with Black Swan Events with the state-of-the-art of augmenting the training data distribution and report that the multi-task approach proves highly effective.

翻译：随着现代通信系统复杂性的增加，机器学习算法已成为研究焦点。然而，在复杂性提升的同时，性能需求也愈发严格。针对未来无线通信所瞄准的关键应用（如医疗领域），严格且可靠的性能保障至关重要，但基础机器学习方法已被证明难以满足此类需求。因此，我们提出了一个问题：这些方法能否被扩展以更好地应对此类应用所施加的要求？本文研究了一个包含必须妥善处理的罕见重大事件的组合资源分配挑战。我们提出将其视为多任务学习问题，从该领域中选取两种方法——弹性权重巩固和梯度情景记忆，并将其集成到基础的参与者-评论家调度器中。我们比较了这两种方法在处理"黑天鹅事件"时的性能，并与最先进的增强训练数据分布方法进行对比，结果表明多任务方法具有高效性。