Abstract: Offline reinforcement learning (RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the ...
Abstract: Learning trustworthy and reliable offline policies presents significant challenges due to the inherent uncertainty in pre-collected datasets. In this article, we propose a novel offline ...
We study the off-dynamics offline reinforcement learning (RL) problem, where the goal is to learn a policy from offline datasets collected from source and target domains with mismatched transition ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback