Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
This paper develops an end-to-end fuzzy encoder-decoder architecture for enhancing vision-based multi-modal deep spiking Q-networks in autonomous driving. The method addresses two core limitations of spiking reinforcement learning: information loss stemming from the conversion of dense visual inputs into sparse spike trains, and the limited representational capacity of spike-based value functions, which often yields weakly discriminative Q-value estimates. The encoder introduces trainable fuzzy membership functions to generate expressive, population-based spike representations, and the decoder uses a lightweight neural decoder to reconstruct continuous Q-values from spiking outputs. Exper...
| Authors | Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou et al. |
|---|---|
| Primary category | cs.NE |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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