Corpus ID: 224705299

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences

@article{Koster2020ModelfreeCI,
  title={Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences},
  author={R. Koster and Kevin R. McKee and Richard Everett and Laura Weidinger and William S. Isaac and Edward Hughes and Edgar A. Du{\'e}{\~n}ez-Guzm{\'a}n and T. Graepel and M. Botvinick and Joel Z. Leibo},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.09054}
}

• R. Koster, Kevin R. McKee, +7 authors Joel Z. Leibo
• Published 2020
• Computer Science
• ArXiv

Game theoretic views of convention generally rest on notions of common knowledge and hyper-rational models of individual behavior. However, decades of work in behavioral economics have questioned the validity of both foundations. Meanwhile, computational neuroscience has contributed a modernized 'dual process' account of decision-making where model-free (MF) reinforcement learning trades off with model-based (MB) reinforcement learning. The former captures habitual and procedural learning while… Expand

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