Qinlong Gu

Papers

Total Citations

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About

Qinlong Gu is a researcher whose work bridges reinforcement learning and neural network architectures, with a particular focus on addressing the "curse of dimensionality" in continuous state-space systems. His most cited contribution, the 2010 paper "Study on Q-learning algorithm based on ART2," introduces a novel integration of the Adaptive Resonance Theory 2 (ART2) neural network into the Q-learning framework. This approach enables more efficient learning in complex, high-dimensional environments by leveraging ART2's ability to dynamically cluster and stabilize input patterns, thereby mitigating the computational explosion typical of traditional Q-learning. While his citation count is modest—with the paper garnering 3 citations—the work represents a meaningful step in making reinforcement learning more scalable for real-world intelligent systems. Gu's research is particularly valuable for students and practitioners exploring hybrid models that combine neural plasticity with temporal-difference learning, offering a foundation for further innovation in adaptive control and autonomous decision-making.