3 citations · 5 across the 4 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024
Data-Incremental Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang
In this work, we propose a new setting of continual learning: data-incremental continual offline reinforcement learning (DICORL), in which an agent is asked to learn a sequence of…
cs.LG2023★ 2 cited
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Jinxin Liu, Ziqi Zhang, Zhenyu Wei +4
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challen…
cs.LG2023★ 3 cited
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Sibo Gai, Donglin Wang, Li He
The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline t…