8 citations · 19 across the 11 of their papers we have counts for
6 papers · 1 filter
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Shenzhi Wang, Qisen Yang, Jiawei Gao +6
Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the i…
Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning
Qisen Yang, Huanqian Wang, Mukun Tong +3
The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications. Therefore, interpreting and explaining RL agents have been active research topic…
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
Qisen Yang, Shenzhi Wang, Qihang Zhang +2
Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributiona…
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…
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…