Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915943088652288 |
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| author | Ghoreishee, Aref Mishra, Abhishek Zhou, Lifeng Walsh, John Das, Anup Kandasamy, Nagarajan |
| author_facet | Ghoreishee, Aref Mishra, Abhishek Zhou, Lifeng Walsh, John Das, Anup Kandasamy, Nagarajan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_16436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving Ghoreishee, Aref Mishra, Abhishek Zhou, Lifeng Walsh, John Das, Anup Kandasamy, Nagarajan Neural and Evolutionary Computing Machine Learning 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. |
| title | Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2604.16436 |