2 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control
Qi Zhao, Guozheng Ma, Yilun Kong +9
Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many…
UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
Jiaxi Wu, Tiantian Zhang, Yuxing Wang +2
Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications…
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Kai Qin, Jiaqi Wu, Jianxiang He +8
As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM un…
Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning
Yifu Luo, Yongzhe Chang, Xueqian Wang
Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focu…
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays
Bo Xia, Yilun Kong, Yongzhe Chang +4
Classic reinforcement learning (RL) frequently confronts challenges in tasks involving delays, which cause a mismatch between received observations and subsequent actions, thereby…
A Method on Searching Better Activation Functions
Haoyuan Sun, Zihao Wu, Bo Xia +5
The success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to mo…