Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets. Recently, due to their powerful representational capabilities, diffusion models have shown significant potential as policy models for offline RL issues. However, previous offline RL algorithms base...

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Hauptverfasser:	Zhang, Tianle, Guan, Jiayi, Zhao, Lin, Li, Yihang, Li, Dongjiang, Zeng, Zecui, Sun, Lei, Chen, Yue, Wei, Xuelong, Li, Lusong, He, Xiaodong
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning
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