Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in beyond fifth generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potential of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.

中文翻译：

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超越5G网络的计算分流：分布式学习框架和应用

面对无线通信和多路访问边缘计算（MEC）融合的趋势，本文研究了第五代以上网络中的计算分流。为了解决MEC系统中由于不确定性和有限资源共享所带来的技术挑战，我们将计算分流问题公式化为多智能体马尔可夫决策过程，为此提出了分布式学习框架。我们目前在计算分流中进行资源编排的案例研究，以展示在该框架下开发的在线分布式强化学习算法的潜力。实验结果表明，我们的学习算法优于基准资源编排算法。此外，