From "Reinforcement Learning: An Introduction":

Radial basis functions (RBFs) are the natural generalization of coarse coding to continuous-valued features. Rather than each feature being either 0 or 1, it can be anything in the interval , reflecting various degrees to which the feature is present. A typical RBF feature has a gaussian (bell-shaped) response, dependent only on the distance between the state, s, and the feature's prototypical or center state and relative to the feature's width: $ \Phi_s(i) = \exp \left( -\frac{||s-c_i||^2}{2\sigma_i^2} \right) $...

In addition, some learning methods for RBF networks change the centers and widths of the features as well.

See also: https://www.math.ucdavis.edu/~saito/data/jim/buhmann-actanumerica.pdf

From sklearn: "For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that **all features are centered around 0 and have variance in the same order**. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected."