14 citations · 67 across the 56 of their papers we have counts for
8 papers · 1 filter
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Yaoquan Wei, Shunyu Liu, Jie Song +4
Action advising endeavors to leverage supplementary guidance from expert teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement Learning (DRL). Previous age…
Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket
Yuwen Wang, Shunyu Liu, Kaixuan Chen +5
Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large…
Curricular Subgoals for Inverse Reinforcement Learning
Shunyu Liu, Yunpeng Qing, Shuqi Xu +6
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success i…
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Kaixuan Chen, Shunyu Liu, Tongtian Zhu +5
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data. However, existing GNNs typically ignore crucial struct…
Message-passing selection: Towards interpretable GNNs for graph classification
Wenda Li, Kaixuan Chen, Shunyu Liu +5
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' b…
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
Yu Wang, Tongya Zheng, Shunyu Liu +4
Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human…
Lookaround Optimizer: steps around, 1 step average
Jiangtao Zhang, Shunyu Liu, Jie Song +3
Weight Average (WA) is an active research topic due to its simplicity in ensembling deep networks and the effectiveness in promoting generalization. Existing weight average approac…
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Yihe Zhou, Shunyu Liu, Yunpeng Qing +4
Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use…