mlsanityData to 7 Oct 2026

Noisy Spiking Actor Network for Exploration

arXiv 2403.04162 ↗First posted 7 Mar 2024cs.LG cs.NEcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

15 of 24 references.

References · 15

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  2. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  5. 2017Noisy Networks for Explorationcited by 262
  6. 2017Parameter Space Noise for Explorationcited by 158
  7. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 149
  8. 2020Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Controlcited by 44
  9. 2020Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardwarecited by 32
  10. 2022Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performancecited by 21
  11. 2023Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networkscited by 17
  12. 2024Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learningcited by 10
3 more
  1. 2020NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Explorationcited by 5
  2. 2023A Novel Noise Injection-based Training Scheme for Better Model Robustnesscited by 3
  3. 2022A noise based novel strategy for faster SNN trainingcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Controlcited by 4
  2. 2025Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learningcited by 0