| Title: |
Noisy Spiking Actor Network for Exploration |
| Authors: |
Chen, Ding, Peng, Peixi, Huang, Tiejun, Tian, Yonghong |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing |
| More Details: |
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.
Comment: 13 pages, 6 figures |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2403.04162 |
| Accession Number: |
edsarx.2403.04162 |
| Database: |
arXiv |
