5 citations · 9 across the 8 of their papers we have counts for
5 papers · 1 filter
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang +4
Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing method…
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
Chengxing Jia, Fuxiang Zhang, Yi-Chen Li +5
Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identificatio…
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
Xu-Hui Liu, Feng Xu, Xinyu Zhang +5
Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from…
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning
Xue-Kun Jin, Xu-Hui Liu, Shengyi Jiang +1
Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value Es…
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang +3
In reinforcement learning, experience replay stores past samples for further reuse. Prioritized sampling is a promising technique to better utilize these samples. Previous criteria…