Space

class eagerx.core.space.Space(low=None, high=None, shape=None, dtype=<class 'numpy.float32'>, seed=None)[source]

A (possibly unbounded) space in R^n. Specifically, a Space 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).

There are two common use cases:

Identical bound for each dimension::

>>> Space(low=-1.0, high=2.0, shape=(3, 4), dtype="float32")
Space(3, 4)

Independent bound for each dimension::

>>> Space(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype="float32")
Space(2,)

contains(x)[source]

Return boolean specifying if x is a valid member of this space :param x: array to check.

Return type: bool

contains_space(space)[source]

Return boolean specifying if space is contained in this space. Low and high of the space must exactly match (instead of lying within the bounds) to return True :type space: Union[Space, Dict] :param space: Space that is to be checked.

Return type: bool

classmethod from_dict(d)[source]

Create a space from a dict.

Parameters: d (Dict) – Dict containing the arguments to initialize the space
Return type: Space
Returns: The space.

from_jsonable(sample_n)[source]: Convert a JSONable data type to a batch of samples from this space.

sample()[source]

Randomly sample an element of this space. Can be uniform or non-uniform sampling based on boundedness of space.

Return type: ndarray

seed(seed=None)[source]

Seed the PRNG of this space and possibly the PRNGs of subspaces.

Return type: list[int]

to_dict()[source]

Convert the space to a dict representation

Return type: Dict
Returns: Dict representation of the space.

to_jsonable(sample_n)[source]: Convert a batch of samples from this space to a JSONable data type.

property is_fully_defined: bool: Check if space is fully defined (i.e. low, high, shape and dtype are all provided). :rtype: bool :return: flag

property is_np_flattenable: Checks whether this space can be flattened.

property np_random: numpy.random._generator.Generator

Lazily seed the PRNG since this is expensive and only needed if sampling from this space.

As seed() is not guaranteed to set the _np_random for particular seeds. We add a check after seed() to set a new random number generator.

Return type: Generator

property shape: tuple[int, ...] | None: Return the shape of the space as an immutable property.