30 citations · 88 across the 30 of their papers we have counts for
7 papers · 1 filter
Learning to Evolve on Dynamic Graphs
Xintao Xiang, Tiancheng Huang, Donglin Wang
Representation learning in dynamic graphs is a challenging problem because the topology of graph and node features vary at different time. This requires the model to be able to eff…
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…
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
Jinxin Liu, Donglin Wang, Qiangxing Tian +1
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions. Considering such p…
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…