Struct coliseum::space::Box

pub struct Box<D: Dimension, Shape: ShapeBuilder<Dim = D>> { /* fields omitted */ }

A (possibly unbounded) box in R^n. Specifically, a Box represents the Cartesian product of n closed intervals. Each interval has the form of one of [a, b], (-oo, b], [a, oo), or (-oo, oo). E.g. low = [-oo, -oo], high=[oo,oo] is a 2D Cartesian plane

Examples

Trait Implementations

impl<D, Shape> Space<ArrayBase<OwnedRepr<f64>, D>> for Box<D, Shape> where
    D: Dimension,
    Shape: ShapeBuilder<Dim = D>, 

fn sample(self) -> ArrayBase<OwnedRepr<f64>, D>

Samples using a uniform distribution with self.low and self.high. We should be able to return a different distribution based on the bounds in Box, e.g. Gym uses:

unbounded   = ~self.bounded_below & ~self.bounded_above
upp_bounded = ~self.bounded_below &  self.bounded_above
low_bounded =  self.bounded_below & ~self.bounded_above
bounded     =  self.bounded_below &  self.bounded_above

#### Vectorized sampling by interval type

sample[unbounded] = self.np_random.normal(
    size=unbounded[unbounded].shape)

sample[low_bounded] = self.np_random.exponential(
    size=low_bounded[low_bounded].shape) + self.low[low_bounded]

....

fn contains(self, sample: Array<f64, D>) -> bool

Whether the sample exists in the Box