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cs.LG2025
Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
Huikang Su, Dengyun Peng, Zifeng Zhuang +4
Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action g…
cs.LG2024★ 1 cited
Reinformer: Max-Return Sequence Modeling for Offline RL
Zifeng Zhuang, Dengyun Peng, Jinxin Liu +2
As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as sequence modeling that conditions on the hindsight information including returns, goal or futu…