9 papers
Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering
Zhongjian Qiao, Rui Yang, Jiafei Lyu +4
Cross-domain offline reinforcement learning (RL) aims to train a well-performing agent in the target environment, leveraging both a limited target domain dataset and a source domai…
Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts
Zhongjian Qiao, Rui Yang, Jiafei Lyu +5
Single-domain offline reinforcement learning (RL) often suffers from limited data coverage, while cross-domain offline RL handles this issue by leveraging additional data from othe…
Temporal Difference Learning with Constrained Initial Representations
Jiafei Lyu, Jingwen Yang, Zhongjian Qiao +5
Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with the environment, including archi…
Cross-Domain Offline Policy Adaptation via Selective Transition Correction
Mengbei Yan, Jiafei Lyu, Shengjie Sun +5
It remains a critical challenge to adapt policies across domains with mismatched dynamics in reinforcement learning (RL). In this paper, we study cross-domain offline RL, where an…
PROF: An LLM-based Reward Code Preference Optimization Framework for Offline Imitation Learning
Shengjie Sun, Jiafei Lyu, Runze Liu +4
Offline imitation learning (offline IL) enables training effective policies without requiring explicit reward annotations. Recent approaches attempt to estimate rewards for unlabel…
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
Zeyuan Liu, Zhihe Yang, Jiawei Xu +5
Real-world datasets collected from sensors or human inputs are prone to noise and errors, posing significant challenges for applying offline reinforcement learning (RL). While exis…