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

• URL: http://arxiv.org/abs/2604.16436v1
• Date: Mon, 06 Apr 2026 20:10:59 GMT
• Title: Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
• Abstract summary: This paper develops an end-to-end fuzzy encoder-decoder architecture for enhancing vision-based deep spiking Q-networks in autonomous driving.<n>The proposed architecture substantially improves decision-making accuracy and closes the performance gap between spiking and non-spiking multi-modal Q-networks.
• Score: 11.138810317218047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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