Freire, Ismael T.Moulin Frier, ClementSanchez Fibla, MartiArsiwalla, Xerxes D.Verschure, Paul F. M. J.2022-01-272022-01-272020-06-221932-6203https://hdl.handle.net/2445/182707What 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.22 p.application/pdfengcc by (c) Freire, Ismael T. et al., 2020http://creativecommons.org/licenses/by/3.0/es/Simulació per ordinadorTeoria de jocsConducta (Psicologia)Human behaviorComputer simulationGame theoryinfo:eu-repo/semantics/article2022-01-25info:eu-repo/semantics/openAccess628799232569266