Shuiying Xiang

Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Supporting Information Filename Description lpor71022-sup-0001-SuppMat.docx465.2 KB Supporting File: lpor71022-sup-0001-SuppMat.docx. References 1, “Neuromorphic Electronic Systems,” Proceedings of the IEEE 78, no. 10 (1990): 1629–1636.

Reinforcement learning (RL) is vital for continuous decision-making in tasks such as robotic control and autonomous driving, yet conventional electronic hardware suffers from high energy consumption and latency due to the von Neumann bottleneck. In this paper, we propose a photonic spiking deep deterministic policy gradient (spiking-DDPG) RL architecture and demonstrate its hardware implementation on a photonic spiking neuromorphic chip (PSNC).