An optimized vecotrized version for the generalized advantage estimation is used in case
gamma and lambda are scalars.

When handling consecutive trajectories of the form
```
reward = [r00, r01, r02, r03, r10, r11]
done = [False, False, False, True, False, False]
```
, `vec_generalized_advantage_estimate`
needs to build a giant famma tensor of size [Batch, T, T] with a decayed gamma
tensor that suits each trajectory. Thus it needs to allocate a big tensor `[B,
T, T]` and do a heavy matrix multiplication.  In case gamma and lambda are
scalars, this can be optimized by building a single tensor of the form
```
r_transformed = [[r00, r01, r02, r03]
		 [r10, r11, 0, 0]]
```
and applying the gamma filter `[r00 + gamma r01 + gamma ** 2 r02 + ..., ro1 + gamma r02 + gamma ** 2 r03 + ...,]`
to calculate the GAE.

* move helper methods to util
* reuse existing helper methods
* remove wip file

In case gamma and lmbda are scalars, `fast_vec_gae` should be always faster than
`vec_generalized_advantage_estimate` if len(T) is large enough.

…lementation"

This reverts commit 245e68f.

in case there is only one split, _inv_pad_sequence can skip its calculation.

…ent.