  Brain inspired spiking neural networks (SNNs) have been successfully applied
to many pattern recognition domains. The SNNs based deep structure have
achieved considerable results in perceptual tasks, such as image
classification, target detection. However, the application of deep SNNs in
reinforcement learning (RL) tasks is still a problem to be explored. Although
there have been previous studies on the combination of SNNs and RL, most of
them focus on robotic control problems with shallow networks or using ANN-SNN
conversion method to implement spiking deep Q Network (SDQN). In this work, we
mathematically analyzed the problem of the disappearance of spiking signal
features in SDQN and proposed a potential based layer normalization(pbLN)
method to directly train spiking deep Q networks. Experiment shows that
compared with state-of-art ANN-SNN conversion method and other SDQN works, the
proposed pbLN spiking deep Q networks (PL-SDQN) achieved better performance on
Atari game tasks.
