For my application, I am considering a learning problem where I simulate a bunch of episodes, say '$n$' first, and than carry out the recursive least squares update. Similar to $TD(1)$.

I know that RLS can be used to update parameters being learned as they arrive. This can be done efficiently for a single data point and the derivations are easily available online and also easy to understand.

However, for my case, I am looking for the same equations when data arrives as a mini batch and not a single data point at a time. I could not find any material regarding RLS for mini batches.

According to my understanding, the same equations can be also used by appropriately considering matrix dimensions. However, I do not know if this is valid.

What are the alternatives to be used?