[metalearn] support continuous policy action space

[cosmicBboy]

Support continuous action space for selecting real hyperparameters within the bounds specified by algorithm space config:

• https://medium.com/@asteinbach/actor-critic-using-deep-rl-continuous-mountain-car-in-tensorflow-4c1fb2110f7c
• use the normal distribution: https://pytorch.org/docs/stable/distributions.html#normal

[cosmicBboy]

To implement this, need to generalize the hyperparameter head to support both multiclass classification over a discrete and finite hyperparameter space, but also a continuous hyperparameter space.

For the continuous case, the heads should be mu and sigma that produce an inference of the parameters that govern the shape of a gaussian. On policy generation, these two values should be used to parameterize a normal distribution that we sample from to produce a scalar of the continuous action (e.g. the l2 regularization parameter value).