A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Deep reinforcement learning with spiking q-learning
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A quantum-enhanced spiking Q-network is reported to outperform classical, spiking, and quantum-dense baselines in small grid-world navigation, with gains that are small relative to the reported error bars.
SwitchMT uses adaptive task-switching in deep spiking Q-networks with active dendrites to reduce task interference in multi-task RL, achieving competitive Atari scores without added network complexity.
citing papers explorer
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
A quantum-enhanced spiking Q-network is reported to outperform classical, spiking, and quantum-dense baselines in small grid-world navigation, with gains that are small relative to the reported error bars.
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Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents
SwitchMT uses adaptive task-switching in deep spiking Q-networks with active dendrites to reduce task interference in multi-task RL, achieving competitive Atari scores without added network complexity.