We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm, that adapts the information flow through the network, without using knowledge of the link capacities or network topology. Numerical evaluations show that our algorithm can achieve the desired rate even in dynamic environments and it is robust against blockage.

翻译：我们认为,一个希望以理想速度与目的地进行沟通的来源,它来自一个以毫米Wave网络,该网络的链接会受到阻塞,节点会发生故障(例如在敌对的军事环境中)。为了实现连结和节点故障的复原力,我们在这里探索一个最先进的SoftAcor-Crictic(SAC)深层强化学习算法,该算法可以调整网络中的信息流动,而不必使用对连接能力或网络地形的了解。数字评估表明,我们的算法即使在动态环境中也能达到理想的速率,而且它能够抵御阻塞。