8 citations · 14 across the 5 of their papers we have counts for
6 papers
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Jinxin Liu, Hao Shen, Donglin Wang +2
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals an…
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain
Yachen Kang, Jinxin Liu, Xin Cao +1
We propose an approach for inverse reinforcement learning from hetero-domain which learns a reward function in the simulator, drawing on the demonstrations from the real world. The…
Adaptive Adversarial Training for Meta Reinforcement Learning
Shiqi Chen, Zhengyu Chen, Donglin Wang
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. In this paper, we attempt to improve the robus…
Pareto Self-Supervised Training for Few-Shot Learning
Zhengyu Chen, Jixie Ge, Heshen Zhan +2
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed…
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning
Ting Wang, Zongkai Wu, Donglin Wang
Vision-and-language navigation (VLN) is a challenging task that requires an agent to navigate in real-world environments by understanding natural language instructions and visual i…
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
Siteng Huang, Min Zhang, Yachen Kang +1
The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recen…