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

Understand

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 the latter captures choices taken via explicit planning and deduction.