Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip

Hardware accelerators based on physical dynamical systems offer an attractive route toward energy-efficient reinforcement learning applications. However, their scalability is challenging because it requires many statistically independent entropy sources. Here, we introduce a quasi-analog decision-ma