Freire, Ismael T. and Moulin-Frier, Clement and Sanchez-Fibla, Marti and Arsiwalla, Xerxes D. and Verschure, Paul F. M. J.
What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent’s reactive behaviors (pre-wired reflexes), along with an Adaptive Layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.
@article{freire2020ModelingFormationSocial,
title = {Modeling the Formation of Social Conventions from Embodied Real-Time Interactions},
author = {Freire, Ismael T. and {Moulin-Frier}, Clement and {Sanchez-Fibla}, Marti and Arsiwalla, Xerxes D. and Verschure, Paul F. M. J.},
year = {2020},
volume = {15},
pages = {e0234434},
publisher = {{Public Library of Science}},
issn = {1932-6203},
doi = {10.1371/journal.pone.0234434},
file = {/Users/moulinfr/Google Drive/work/CRCN/readings/zotero/Freire et al_2020_Modeling the formation of social conventions from embodied real-time.pdf},
journal = {PLOS ONE},
number = {6}
}
