---
res:
  bibo_abstract:
  - "This work considers the problem of control and resource allocation in networked\r\nsystems.
    To this end, we present DIRA a Deep reinforcement learning based Iterative Resource\r\nAllocation
    algorithm, which is scalable and control-aware. Our algorithm is tailored towards\r\nlarge-scale
    problems where control and scheduling need to act jointly to optimize performance.\r\nDIRA
    can be used to schedule general time-domain optimization based controllers. In
    the present\r\nwork, we focus on control designs based on suitably adapted linear
    quadratic regulators. We\r\napply our algorithm to networked systems with correlated
    fading communication channels. Our\r\nsimulations show that DIRA scales well to
    large scheduling problems.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Adrian
      foaf_name: Redder, Adrian
      foaf_surname: Redder
      foaf_workInfoHomepage: http://www.librecat.org/personId=52265
    orcid: https://orcid.org/0000-0001-7391-4688
  - foaf_Person:
      foaf_givenName: Arunselvan
      foaf_name: Ramaswamy, Arunselvan
      foaf_surname: Ramaswamy
      foaf_workInfoHomepage: http://www.librecat.org/personId=66937
    orcid: https://orcid.org/ 0000-0001-7547-8111
  - foaf_Person:
      foaf_givenName: Daniel
      foaf_name: Quevedo, Daniel
      foaf_surname: Quevedo
  dct_date: 2019^xs_gYear
  dct_language: eng
  dct_subject:
  - Networked control systems
  - deep reinforcement learning
  - large-scale systems
  - resource scheduling
  - stochastic control
  dct_title: Deep reinforcement learning for scheduling in large-scale networked control
    systems@
...