Noisy Spiking Actor Network for Exploration
Ding Chen Peixi Peng Tiejun Huang Yonghong Tian
As a general method for exploration in deep reinforcement learning (RL),
NoisyNet can produce problem-specific exploration strategies. Spiking neural
networks (SNNs), due to their binary firing mechanism, have strong robustness
to noise, making it difficult to realize efficient exploration with local
disturbances. To solve this exploration problem, we propose a noisy spiking
actor network (NoisySAN) that introduces time-correlated noise during charging
and transmission. Moreover, a noise reduction method is proposed to find a
stable policy for the agent. Extensive experimental results demonstrate that
our method outperforms the state-of-the-art performance on a wide range of
continuous control tasks from OpenAI gym.