AI RESEARCH
Abstraction for Offline Goal-Conditioned Reinforcement Learning
arXiv CS.AI
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ArXi:2605.22711v1 Announce Type: cross Marko Decision Processes (MDPs) often exhibit significant redundancy due to symmetries and shared structure across state-goal pairs in real-world Goal-Conditioned Reinforcement Learning (GCRL). While hierarchical policies have been motivated for horizon reduction via temporal abstraction in offline GCRL, we nstrate that hierarchy also enables absolute abstraction. By