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The Challenges of Exploration for Offline Reinforcement Learning
[article]
2022
arXiv
pre-print
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour. The second step has been widely studied in the offline setting, but just as critical to data-efficient RL is the collection of informative data. The task-agnostic setting for data collection, where the task is not known a priori, is of particular interest due to the possibility of collecting a single
arXiv:2201.11861v2
fatcat:sajzyrnxuze6lo2lozj4szy4um