Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care. (arXiv:2111.06680v2 [cs.LG] UPDATED)

Greater capabilities of mobile communications technology enable
interconnection of on-site medical care at a scale previously unavailable.
However, embedding such critical, demanding tasks into the already complex
infrastructure of mobile communications proves challenging. This paper explores
a resource allocation scenario where a scheduler must balance mixed performance
metrics among connected users. To fulfill this resource allocation task, we
present a scheduler that adaptively switches between different model-based
scheduling algorithms. We make use of a deep Q-Network to learn the benefit …

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