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

ArXi:2604.16436v1 Announce Type: cross 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.