When the dynamical writing of coupled memories with reinforcement learning meets physical bounds

Abstract

Manipulating binary digits of information (bits) is a prerequisite for reliable memory utilization. The authors present a dynamical framework in which a reinforcement learning agent harnesses the physics of simple multi-bit mechanical models to restore their memory, suggesting new optimal system designs.