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Ming-Jing Xu

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#artificial intelligence Preprint Oct 2026

Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning

With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient and fails to exploit the potential existence of a suboptimal expert, such as a heuristic, a model-based controller, or a policy trained on a...

D. Affinita, Ming-Jing Xu, Rudolf Reiter et al. · 0 citations

#machine learning Preprint Sep 2026

Probabilistic Robustness-driven Universal Adversarial Perturbations with Explainability against Deep Reinforcement Learning-based Intrusion Detection System

Deep reinforcement learning (DRL) enables adaptive intrusion detection in dynamic network environments but also exposes intrusion detection systems (IDS) to adversarial threats such as universal adversarial perturbations (UAPs), which apply a single input-agnostic perturbation to degrade detection performance across tr...

Hong-Sen Zhang, Lu Zhang, Ming-Jing Xu et al. · 0 citations