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cs.LG2024
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy. Offline RL aims to solve this issue by using transitions colle…
cs.LG2024
Offline Reinforcement Learning with Domain-Unlabeled Data
Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann +1
Offline reinforcement learning (RL) is vital in areas where active data collection is expensive or infeasible, such as robotics or healthcare. In the real world, offline datasets o…