A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation
This addresses the problem of reliable performance in resource allocation for critical applications like wireless medical systems, but it is incremental as it adapts existing multi-task methods to a specific domain.
The paper tackled the challenge of robust deep reinforcement learning for resource allocation in complex communication systems, particularly for applications like medical fields requiring strict performance guarantees, by proposing a multi-task learning approach using Elastic Weight Consolidation and Gradient Episodic Memory integrated into an actor-critic scheduler, and found it highly effective compared to augmenting training data distribution.
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.