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Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms

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  • معلومة اضافية
    • Contributors:
      Systèmes Cognitifs et Systèmes Multi-Agents (SyCoSMA); Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS); Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS); Département Informatique et systèmes intelligents ( FAYOL-ENSMSE); Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE); Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS); Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne); Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA); Institut Henri Fayol (FAYOL-ENSMSE); École des Mines de Saint-Étienne (Mines Saint-Étienne MSE); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); UR CONFLUENCE : Sciences et Humanités (EA 1598); UCLy (Lyon Catholic University) (UCLy); This work was funded by Région Auvergne-Rhône-Alpes (AURA), as part of Project Ethics.AI (Pack Ambition Recherche).; Ethics.AI
    • بيانات النشر:
      HAL CCSD
    • الموضوع:
      2023
    • Collection:
      HAL Lyon 1 (University Claude Bernard Lyon 1)
    • نبذة مختصرة :
      The numerous deployed Artificial Intelligence systems need to be aligned with our ethical considerations. However, such ethical considerations might change as time passes: our society is not fixed, and our social mores evolve. This makes it difficult for these AI systems; in the Machine Ethics field especially, it has remained an under-studied challenge. In this paper, we present two algorithms, named QSOM and QDSOM, which are able to adapt to changes in the environment, and especially in the reward function, which represents the ethical considerations that we want these systems to be aligned with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps to handle the continuous and multi-dimensional state and action spaces. We evaluate them on a use-case of multi-agent energy repartition within a small Smart Grid neighborhood, and prove their ability to adapt, and their higher performance compared to baseline Reinforcement Learning algorithms.
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/2307.00552; hal-04379426; https://hal.science/hal-04379426; https://hal.science/hal-04379426/document; https://hal.science/hal-04379426/file/2307.00552.pdf; ARXIV: 2307.00552
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.1905B3CF