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Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
Preprint   Open access

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving

Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou, John Walsh, Anup Das and Nagarajan Kandasamy
06 Apr 2026
DOI: https://doi.org/10.48550/arxiv.2604.16436
url
https://doi.org/10.48550/arxiv.2604.16436View
Preprint (Author's original) Open arXiv.org - Non-exclusive license to distribute

Abstract

Computer Science - Learning Computer Science - Neural and Evolutionary Computing Computer Science
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. Experiments on the HighwayEnv benchmark show that the proposed architecture substantially improves decision-making accuracy and closes the performance gap between spiking and non-spiking multi-modal Q-networks. The results highlight the potential of this framework for efficient and real-time autonomous driving with spiking neural networks.

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