Deep reinforcement learning for scheduling in large-scale networked control systems Redder, Adrian Ramaswamy, Arunselvan Quevedo, Daniel Networked control systems deep reinforcement learning large-scale systems resource scheduling stochastic control ddc:620 This work considers the problem of control and resource allocation in networked systems. To this end, we present DIRA a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems. 2019 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/13443 Redder A, Ramaswamy A, Quevedo D. Deep reinforcement learning for scheduling in large-scale networked control systems. In: <i>Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems</i>. ; 2019. eng info:eu-repo/semantics/openAccess