  Spiking neural network (SNN), compared with depth neural network (DNN), has
faster processing speed, lower energy consumption and more biological
interpretability, which is expected to approach Strong AI. Reinforcement
learning is similar to learning in biology. It is of great significance to
study the combination of SNN and RL. We propose the reinforcement learning
method of spike distillation network (SDN) with STBP. This method uses
distillation to effectively avoid the weakness of STBP, which can achieve SOTA
performance in classification, and can obtain a smaller, faster convergence and
lower power consumption SNN reinforcement learning model. Experiments show that
our method can converge faster than traditional SNN reinforcement learning and
DNN reinforcement learning methods, about 1000 epochs faster, and obtain SNN
200 times smaller than DNN. We also deploy SDN to the PKU nc64c chip, which
proves that SDN has lower power consumption than DNN, and the power consumption
of SDN is more than 600 times lower than DNN on large-scale devices. SDN
provides a new way of SNN reinforcement learning, and can achieve SOTA
performance, which proves the possibility of further development of SNN
reinforcement learning.
