Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-fuzzy-spiking-q-learning-autonomous-drivinggit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-fuzzy-spiking-q-learning-autonomous-driving/SKILL.md--- 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