7 papers · 1 filter
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging
Yichu Xu, Xin-Chun Li, Le Gan +1
Merging models becomes a fundamental procedure in some applications that consider model efficiency and robustness. The training randomness or Non-I.I.D. data poses a huge challenge…
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Shenghua Wan, Ziyuan Chen, Le Gan +2
Model-based offline reinforcement Learning (RL) is a promising approach that leverages existing data effectively in many real-world applications, especially those involving high-di…
DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
Hanye Zhao, Xiaoshen Han, Zhengbang Zhu +4
With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. M…
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
Wen-Shu Fan, Xin-Chun Li, De-Chuan Zhan
Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and so…
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
Kaichen Huang, Minghao Shao, Shenghua Wan +4
In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation…
DIDA: Denoised Imitation Learning based on Domain Adaptation
Kaichen Huang, Hai-Hang Sun, Shenghua Wan +4
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on th…
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Yucen Wang, Shenghua Wan, Le Gan +2
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous…