21 citations · 28 across the 11 of their papers we have counts for
11 papers
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
Yao Lai, Yao Mu, Ping Luo
Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of…
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
Junjie Wang, Yao Mu, Dong Li +6
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse…
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Yao Mu, Yuzheng Zhuang, Fei Ni +4
Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learn…
Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mu, Ping Luo +2
Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved,…
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Qiushan Guo, Yao Mu, Jianyu Chen +3
Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory s…
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
Zhecheng Yuan, Guozheng Ma, Yao Mu +5
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at en…