A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.
A multimodal deep reinforcement learning approach for iot-driven adaptive scheduling and robust- ness optimization in global logistics networks
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Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.