Before revision:  To advance in this direction, we propose, within the framework of the Distributed Adaptive Control (DAC) theory , a novel Control-based Reinforcement Learning architecture (CRL) that can account for the acquisition of social conventions in multi-agent populations that are solving a benchmark social decision-making problem. Our new CRL architecture, as a concrete realization of DAC multi-agent theory, implements a low-level sensorimotor control loop handling the agent's reactive behaviors (pre-wired reflexes), along with a layer based on model-free reinforcement learning that maximizes long-term reward.
After revision:  To advance in this direction, we propose, within the framework of the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control loop handling the agent's reactive behaviors (pre-wired reflexes), along with a layer based on model-free reinforcement learning that maximizes long-term reward.