AI brief
A question on AI Stack Exchange asks how importance sampling compares with policy regularization in offline reinforcement learning, describing offline RL as using a dataset of episode transitions generated by a behavior policy that may be random or optimized.
Written by AI from AI Stack Exchange's published text.
Source
“In offline RL we are usually equipped with a dataset, often also called Replay Buffer, that contains transitions in an episode, that are created by some behavior policy.”
Interpretation or community commentary rather than straight reporting.
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