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This repository has been archived by the owner on Jun 13, 2024. It is now read-only.
It is doable (a few hours effort), but not straightforward (< 20 minutes).
This is because:
the function get_optimistic_exploration_action requires computing one backward pass and per-sample gradient (and not sum of gradients over a batch of input).
AFAIK, pytorch doesn't support computing per-sample gradient.
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Would it be straighforward to implement a batched version of
get_optimistic_exploration_action
?The text was updated successfully, but these errors were encountered: