3 papers
cs.LG2026
Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets
Zhongjian Qiao, Jiafei Lyu, Chenjia Bai +3
Cross-domain offline reinforcement learning (RL) aims to learn a policy in the target domain with a limited target domain dataset and a source domain dataset that exhibits a dynami…
cs.LG2026
Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting
Zhongjian Qiao, Jiafei Lyu, Boxiang Lyu +3
Model-based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, \textit{model exploitation} could occ…
cs.LG2025
Sample-Efficient Policy Constraint Offline Deep Reinforcement Learning based on Sample Filtering
Yuanhao Chen, Qi Liu, Pengbin Chen +2
Offline reinforcement learning (RL) aims to learn a policy that maximizes the expected return using a given static dataset of transitions. However, offline RL faces the distributio…