面向网络切片编排的多智能体强化学习体系结构

Federico Mason, G. Nencioni, A. Zanella
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引用次数: 3

摘要

网络切片(NS)范式是未来5G网络的支柱之一,正受到工业界和科学界的高度关注。在NS场景中,物理资源和虚拟资源被划分在多个逻辑网络中,这些逻辑网络被称为片,具有特定的特征。挑战在于找到有效的策略来根据用户需求在不同的片之间动态分配网络资源。在本文中,我们通过利用深度强化学习方法来解决目标问题。我们的框架基于分布式体系结构,其中多个代理协作实现共同目标。智能体的训练遵循了优势Actor批评家算法,使得处理连续动作空间成为可能。通过大量的模拟,我们证明了我们的策略比有效的经验算法产生更好的性能,同时确保了对不同场景的高适应性,而无需额外的训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Agent Reinforcement Learning Architecture for Network Slicing Orchestration
The Network Slicing (NS) paradigm is one of the pillars of the future 5G networks and is gathering great attention from both industry and scientific communities. In a NS scenario, physical and virtual resources are partitioned among multiple logical networks, named slices, with specific characteristics. The challenge consists in finding efficient strategies to dynamically allocate the network resources among the different slices according to the user requirements. In this paper, we tackle the target problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which makes it possible to handle continuous action spaces. By means of extensive simulations, we show that our strategy yields better performance than an efficient empirical algorithm, while ensuring high adaptability to different scenarios without the need for additional training.
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