35 citations · 321 across the 48 of their papers we have counts for
11 papers · 1 filter
Learning Specialized Activation Functions for Physics-informed Neural Networks
Honghui Wang, Lu Lu, Shiji Song +1
Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and a…
ExpeL: LLM Agents Are Experiential Learners
Andrew Zhao, Daniel Huang, Quentin Xu +3
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs.…
Boosting Offline Reinforcement Learning with Action Preference Query
Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin +2
Training practical agents usually involve offline and online reinforcement learning (RL) to balance the policy's performance and interaction costs. In particular, online fine-tunin…
Decoupled Prioritized Resampling for Offline RL
Yang Yue, Bingyi Kang, Xiao Ma +4
Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constr…
Convolution-enhanced Evolving Attention Networks
Yujing Wang, Yaming Yang, Zhuo Li +7
Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series anal…
Boosting Offline Reinforcement Learning via Data Rebalancing
Yang Yue, Bingyi Kang, Xiao Ma +3
Offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. To address this problem, existing works mainly focus on design…
A Mixture of Surprises for Unsupervised Reinforcement Learning
Andrew Zhao, Matthieu Gaetan Lin, Yangguang Li +2
Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free manner for fast adaptation to downstream tasks. Most of the existing methods propose to pr…
Efficient Knowledge Distillation from Model Checkpoints
Chaofei Wang, Qisen Yang, Rui Huang +2
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong…
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Yang Yue, Bingyi Kang, Zhongwen Xu +2
Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual r…
Provable General Function Class Representation Learning in Multitask Bandits and MDPs
Rui Lu, Andrew Zhao, Simon S. Du +1
While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it…
On the Power of Multitask Representation Learning in Linear MDP
Rui Lu, Gao Huang, Simon S. Du
While multitask representation learning has become a popular approach in reinforcement learning (RL), theoretical understanding of why and when it works remains limited. This paper…