In my opinion the right definition of an episode should be a batch of tasks (usually called a meta-batch). For regression if we have 100 𝑓𝑖
f
i
from some family (e.g. sine functions) then 1 episode with a meta-batch size of size 16 should be 16 functions, each with a support set and a query set.
For classification an episode is still a (meta) batch of tasks. In this case a task is a N-way K-shot classification task. e.g. 5-way, 5-shot would have 25 examples for the support set and if the Keval is 15 then 75 examples for the query set. In this case if we have meta-batch size of 16 then we sample 16 tasks, each with 25+75 examples. So a total of 16*100 examples for a meta-batch.
In fact with this definition 1 episode is the same as an iteration step. When meta-batch size is 1 then a task is an episode.
I can’t imagine why we’d define an episode as a task, which I thought at some point. In that case we have the same word for task and episode. But an episode of learning happens fully during each iteration.
Though, I’d prefer to not use this word at all since it seems redundant + RL already uses this term which adds to the confusion in my opinion.