A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems

The article presents SDQN-RMFS, a neuromorphic reinforcement learning framework designed for efficient pathfinding in Robotic Mobile Fulfillment Systems (RMFS). This framework employs a full-precision artificial neural network that is converted into a spiking neural network through knowledge distillation, achieving up to 11,281× energy savings and nearly half the latency compared to traditional GPU implementations, while preserving decision quality. This advancement is significant for practitioners as it enables the deployment of RL policies on resource-constrained hardware, addressing energy efficiency and real-time performance challenges in dynamic environments.