Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0

  • Guilherme T. T. Bernardo UFMG
  • Gilson Miranda Jr. UFMG / University of Antwerp
  • Daniel F. Macedo UFMG


Dentre os três principais casos de uso das redes 5G está a comunicação ultra-confiável de baixa latência, que inclui a operação remota para a Indústria 4.0. Nesse tipo de aplicação, robôs autônomos são capazes de operar sem intervenção humana, guiados por sistemas de inteligência artificial executados em nuvem. Este artigo investiga dois diferentes aspectos: (i) a performance de um modelo pré-treinado em condições de rede ideais; e (ii) como variações de desempenho da rede influenciam o desempenho dos modelos no ambiente. Os resultados mostram a influência de diferentes condições de qualidade da rede no desempenho do agente, assim como o benefício de utilizar agentes pré-treinados em condições ideais.


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BERNARDO, Guilherme T. T.; MIRANDA JR., Gilson; MACEDO, Daniel F.. Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 1-14. ISSN 2177-9384. DOI:

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