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cs.LG2025
PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
Dongchi Huang, Jiaqi Wang, Yang Li +3
Partial observability presents a significant challenge for Safe Reinforcement Learning (Safe RL), as it impedes the identification of potential risks and rewards. Leveraging specif…
cs.LG2024
SafeDreamer: Safe Reinforcement Learning with World Models
Weidong Huang, Jiaming Ji, Chunhe Xia +2
The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria. Existing Safe Reinforcement Learning (SafeRL) met…