30 citations · 88 across the 30 of their papers we have counts for
18 papers · 1 filter
Learning How to Propagate Messages in Graph Neural Networks
Teng Xiao, Zhengyu Chen, Donglin Wang +1
This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the…
A General Offline Reinforcement Learning Framework for Interactive Recommendation
Teng Xiao, Donglin Wang
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments. We address the problem by proposing…
Learning on Graphs with Out-of-Distribution Nodes
Yu Song, Donglin Wang
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to gra…
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Yachen Kang, Li He, Jinxin Liu +2
Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires consid…
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Jinxin Liu, Ziqi Zhang, Zhenyu Wei +4
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challen…
Beyond Reward: Offline Preference-guided Policy Optimization
Yachen Kang, Diyuan Shi, Jinxin Liu +2
This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online…
CEIL: Generalized Contextual Imitation Learning
Jinxin Liu, Li He, Yachen Kang +3
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired…
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Jinxin Liu, Hongyin Zhang, Zifeng Zhuang +3
In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and…
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Jinxin Liu, Lipeng Zu, Li He +1
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. Howev…
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang, Li He
The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline t…
Behavior Proximal Policy Optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu +2
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-ac…
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
Jinxin Liu, Hongyin Zhang, Donglin Wang
Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. However, such formulation i…
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…
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…
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks
Tiancheng Huang, Ke Xu, Donglin Wang
Domain adaptation using graph-structured networks learns label-discriminative and network-invariant node embeddings by sharing graph parameters. Most existing works focus on domain…