Fuzzy Spiking Q Learning Autonomous Driving

---
name: fuzzy-spiking-q-learning-autonomous-driving
description: "Fuzzy encoder-decoder for spiking Q-networks in autonomous driving. Trainable fuzzy membership functions generate population-based spike representations. Closes performance gap between spiking and non-spiking networks. Activation: spiking Q-learning, fuzzy encoding, autonomous driving, SNN reinforcement learning."
---

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

> arXiv:2604.16436 — Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou, John Walsh, Anup Das, Nagarajan Kandasamy

## Metadata
- **Source**: arXiv:2604.16436
- **Authors**: Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou, John Walsh, Anup Das, Nagarajan Kandasamy
- **Published**: 2025-04
- **Relevance**: medium
- **URL**: https://arxiv.org/abs/2604.16436

## Core Methodology

### Key Innovation
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 

### Technical Framework
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.

## Implementation Guide

### Prerequisites
- Python environment with scientific computing libraries
- Access to paper's supplementary materials at https://arxiv.org/abs/2604.16436

### Step-by-Step
1. Read the full paper at https://arxiv.org/abs/2604.16436
2. Identify the core algorithm/framework from the methodology section
3. Implement the key components as described in the paper
4. Validate using the paper's reported benchmarks

## Applications
- Neuroscience research
- Computational neuroscience
- Neural network design and optimization

## Pitfalls
- Results may be preliminary (preprint)
- Reproducibility depends on availability of code/data

## Related Skills
- computational-neuroscience-models
- neural-population-dynamics
- spiking-neural-network-training