Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning

In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improv...

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Hauptverfasser:	Liu, Tenglong, Li, Yang, Lan, Yixing, Gao, Hao, Pan, Wei, Xu, Xin
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Robotics
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