QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.
Quantum-enhanced hybrid reinforcement learning framework for dynamic path planning in autonomous systems,
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Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
QSNN agent in Q-SpiRL framework achieves up to 99% success rate with efficient paths in 20x20 to 40x40 grid worlds with static and dynamic obstacles, outperforming tabular Q-learning, MLP, SNN, and QMLP baselines under unified evaluation.