Any differences in regularisation in MLP between batch and individual updates?
Regularization is relevant in per-item learning as well. I would suggest to start with a basic validation approach for finding out lambda, whether you are doing batch or per-item learning. This is the easiest and safest approach. Try manually with a number of different values. e.g. 0.001. 0.003, 0.01, 0.03, 0.1 etc. and see how your validation set behaves. Later on you may automate this process by introducing a linear or local search method. 

As a side note, I believe the value of lambda should be considered in relation to the updates of the parameter vector, rather than the training set size. For batch training you have one parameter update per dataset pass, while for online one update per sample (regardless of the training set size).

I recently stumbled upon this Crossvalidated Question, which seems quite similar to yours. There is a link to a paper about a new SGD algorithm, with some relevant content. It might be useful to take a look (especially pages 1742-1743).