RMSProp learning rule implementation

The implementation is based heavily on the AdaDelta learning rule.  Tests still needed -- should hopefully be able to get around to that over the course of the next day.  

The motivations and math behind RMSProp are described by Geoffrey Hinton at the lecture slides below: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.
As far as I can tell, `lr_scalers` are always floats (at least in the maxout and DBM models)?  Also, all other uses of `lr_scalers` in pylearn2 seem to use the same `lr_scalers.get(param, 1.)` expression (see [AdaDelta](https://github.com/madisonmay/pylearn2/blob/master/pylearn2/training_algorithms/learning_rule.py#L126) and [Momentum](https://github.com/madisonmay/pylearn2/blob/master/pylearn2/training_algorithms/learning_rule.py#L240)).