2 citations · 2 across the 6 of their papers we have counts for
6 papers
Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning
Zhiqiang Dong, Teng Pang, Rongjian Xu +1
Offline goal-conditioned reinforcement learning (GCRL) is a practical reinforcement learning paradigm that aims to learn goal-conditioned policies from reward-free offline data. De…
Equivariant Efficient Joint Discrete and Continuous MeanFlow for Molecular Graph Generation
Rongjian Xu, Teng Pang, Zhiqiang Dong +1
Graph-structured data jointly contain discrete topology and continuous geometry, which poses fundamental challenges for generative modeling due to heterogeneous distributions, inco…
Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning
Teng Pang, Zhiqiang Dong, Yan Zhang +3
Offline multi-agent reinforcement learning (MARL) aims to learn the optimal joint policy from pre-collected datasets, requiring a trade-off between maximizing global returns and mi…
Diffusion Classifier-Driven Reward for Offline Preference-based Reinforcement Learning
Teng Pang, Bingzheng Wang, Guoqiang Wu +1
Offline preference-based reinforcement learning (PbRL) mitigates the need for reward definition, aligning with human preferences via preference-driven reward feedback without inter…
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
Yan Zhang, Guoqiang Wu, Bingzheng Wang +3
In Continual Learning (CL), while existing work primarily focuses on the multi-class classification task, there has been limited research on Multi-Label Learning (MLL). In practice…
DiffAIL: Diffusion Adversarial Imitation Learning
Bingzheng Wang, Guoqiang Wu, Teng Pang +2
Imitation learning aims to solve the problem of defining reward functions in real-world decision-making tasks. The current popular approach is the Adversarial Imitation Learning (A…