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Spiking neural network (SNN), compared with depth neural network (DNN), hasfaster processing speed, lower energy consumption and more biologicalinterpretability, which is expected to approach Strong AI. Reinforcementlearning is similar to learning in biology. It is of great significance tostudy the combination of SNN and RL. We propose the reinforcement learningmethod of spike distillation network (SDN) with STBP. This method usesdistillation to effectively avoid the weakness of STBP, which can achieve SOTAperformance in classification, and can obtain a smaller, faster convergence andlower power consumption SNN reinforcement learning model. Experiments show thatour method can converge faster than traditional SNN reinforcement learning andDNN reinforcement learning methods, about 1000 epochs faster, and obtain SNN200 times smaller than DNN. We also deploy SDN to the PKU nc64c chip, whichproves that SDN has lower power consumption than DNN, and the power consumptionof SDN is more than 600 times lower than DNN on large-scale devices. SDNprovides a new way of SNN reinforcement learning, and can achieve SOTAperformance, which proves the possibility of further development of SNNreinforcement learning.

Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents