4 papers
MHWA: Multi-timescale Hierarchical World-Action Model
Pengcheng Pan, Guoqing Ma, Yuhan Zhang +4
Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increasing drift that leads to erro…
Clustering-Based Weight Orthogonalization for Stabilizing Deep Reinforcement Learning
Guoqing Ma, Yuhan Zhang, Yuming Dai +3
Reinforcement learning (RL) has made significant advancements, achieving superhuman performance in various tasks. However, RL agents often operate under the assumption of environme…
Multi-dimensional Neural Decoding with Orthogonal Representations for Brain-Computer Interfaces
Kaixi Tian, Shengjia Zhao, Yuhan Zhang +1
Current brain-computer interfaces primarily decode single motor variables, limiting their ability to support natural, high-bandwidth neural control that requires simultaneous extra…
Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder
Yuhan Zhang, Guoqing Ma, Guangfu Hao +3
While Reinforcement Learning (RL) agents can successfully learn to handle complex tasks, effectively generalizing acquired skills to unfamiliar settings remains a challenge. One of…