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 single data point and the derivations are easily available online and also easy to understand.
However for my case I am looking for same equations when data arrive 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?
