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Abstract: Offline reinforcement learning (RL) has been widely used in practice due to its efficient data utilization, but it still faces the challenge of training vulnerability caused by policy ...

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 ...

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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