# #StackBounty: #meta-learning What does the term episode mean in meta-learning?

### Bounty: 50

Recall in meta-learning we have a meta-set which is a data-set of data-sets:

$$D_{meta-set} = { D_n }^N_{n=1}$$

where $$D_n$$ is data-set (or usually a task). Usually defined as a data sampled from a target function for regression or N classes for a classification task. Usually these individual data sets $$D_n$$ are split into a support set (train set) and a query set (test set).

I’ve seen the term episode used in meta-learning but it’s not been clear to me. There are two possible definitions:

1. 1 episode means sampling 1 single data set $$D_n$$
2. 1 episode means sampling M data-sets. i.e. sampling a batch of tasks

which one is it?

reference:

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