One LSTM layer should be enough unless you have lots of data. The same thing goes for the number of nodes in the layer. Start small first so 5 to 10 nodes and increment it until the performance is reasonable.
Once you have a model working you can apply regularization if you think it will improve performance by reducing overfitting of the training data. You can check this by looking at the learning curves or compring the error on the validation and test sets.
In my experiments I've used the L1 and L2 regularizers along with dropout. These can all be mixed together in fact using both L1 and L2 at the same time is called the ElasticNet.
I tend to apply the regularizers on the
kernel_regularizer because this affects the weights for the inputs. Basically feature selection.
The value for the L1 and L2 can start with the default (for tensorflow) of 0.01 and change it as you see fit or read what other research papers have done.
Dropout can start at 0.1 then increment it until there is no performance gain. This is basically a percentage so 0.1 would remove about 10% of your nodes.
Finding the best regularizer is the same as any other hyperparameter optimization which is mostly trial and error.