  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.
