10 citations · 20 across the 7 of their papers we have counts for
7 papers
Are Expressive Models Truly Necessary for Offline RL?
Guan Wang, Haoyi Niu, Jianxiong Li +3
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL…
Hummer: Towards Limited Competitive Preference Dataset
Li Jiang, Yusen Wu, Junwu Xiong +6
Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedb…
Offline Reinforcement Learning with Imbalanced Datasets
Li Jiang, Sijie Cheng, Jielin Qiu +3
The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development…
MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. However, this task is challenging due to the divers…
A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies
Li Jiang, Ting Zhang, Qiruyi Zuo +4
Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time. Despite the vast amoun…
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Haoran Xu, Li Jiang, Jianxiong Li +4
Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the devi…
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Haoran Xu, Li Jiang, Jianxiong Li +1
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution…