94 citations · 206 across the 53 of their papers we have counts for
7 papers · 1 filter
Extreme Value Policy Optimization for Safe Reinforcement Learning
Shiqing Gao, Yihang Zhou, Shuai Shao +5
Ensuring safety is a critical challenge in applying Reinforcement Learning (RL) to real-world scenarios. Constrained Reinforcement Learning (CRL) addresses this by maximizing retur…
Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration
Shiqing Gao, Jiaxin Ding, Luoyi Fu +1
Constrained Reinforcement Learning (CRL) aims to maximize cumulative rewards while satisfying constraints. However, existing CRL algorithms often encounter significant constraint v…
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints
Shiqing Gao, Jiaxin Ding, Luoyi Fu +2
In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been…
OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
Bin Lu, Ze Zhao, Luyu Han +7
Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fa…
Characterizing the Influence of Topology on Graph Learning Tasks
Kailong Wu, Yule Xie, Jiaxin Ding +4
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the…
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Bin Lu, Xiaoying Gan, Ze Zhao +4
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., traini…