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 ...

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited ...

Abstract: Multiobjective reinforcement learning (MORL) addresses sequential decision-making problems with multiple objectives by learning policies optimized for diverse pReferences. While traditional ...

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