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Published in KDD, 2023
We introduce a novel framework that synergizes deep RL and rule learning to handle hybrid pixel-symbolic inputs, achieving superior performance and transferability.
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Published in AAAI, 2024
We boost the performance of reinforcement learning under visual perturbations by introducing a segmentation model.
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Published in AAMAS, 2024
We propose an active anomaly searching mechanism based on Reinforcement Learning, achieving competitive performance in semi-supervised anomaly detection tasks.
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Published in Neurips, 2025
We propose an object-filter to extract task-relevant information at the object level, enabling rapid cross-domain policy deployment.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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