An episode is considered to be a batch of tasks. For example, an episode may be 5 classes, where we have 5 images per class known as our support set. We also have some number of query images in which we classify as one of those 5 classes. This is what’s considered an episode.
So we essentially have a mini train-set (our support set) and a mini test-set (our query set). This is where the meta-learning concept comes in.
Depending on your set-up, your episode can be sampled from several datasets (also known as domains) or a single dataset. In my experience, it’s typical to sample an episode from a single domain. The idea is that it’s more realistic to have your model adapt to a single unseen domain rather than several unseen domains.
I hope this helps your understanding.