tf_agents.specs.BoundedArraySpec

An ArraySpec that specifies minimum and maximum values.

Inherits From: ArraySpec

View aliases

Main aliases

tf_agents.specs.array_spec.BoundedArraySpec

tf_agents.specs.BoundedArraySpec(
    *args, **kwargs
)

Used in the notebooks

Used in the tutorials
Networks Environments Policies

Example usage:

# Specifying the same minimum and maximum for every element.
spec = BoundedArraySpec((3, 4), np.float64, minimum=0.0, maximum=1.0)

# Specifying a different minimum and maximum for each element.
spec = BoundedArraySpec(
    (2,), np.float64, minimum=[0.1, 0.2], maximum=[0.9, 0.9])

# Specifying the same minimum and a different maximum for each element.
spec = BoundedArraySpec(
    (3,), np.float64, minimum=-10.0, maximum=[4.0, 5.0, 3.0])

Bounds are meant to be inclusive. This is especially important for integer types. The following spec will be satisfied by arrays with values in the set {0, 1, 2}:

spec = BoundedArraySpec((3, 4), np.int, minimum=0, maximum=2)

Args:

shape: An iterable specifying the array shape.
dtype: numpy dtype or string specifying the array dtype.
minimum: Number or sequence specifying the maximum element bounds (inclusive). Must be broadcastable to shape.
maximum: Number or sequence specifying the maximum element bounds (inclusive). Must be broadcastable to shape.
name: Optional string containing a semantic name for the corresponding array. Defaults to None.

Attributes:

dtype: Returns a numpy dtype specifying the array dtype.
maximum: Returns a NumPy array specifying the maximum bounds (inclusive).
minimum: Returns a NumPy array specifying the minimum bounds (inclusive).
name: Returns the name of the ArraySpec.
shape: Returns a tuple specifying the array shape.

Raises:

ValueError: If minimum or maximum are not broadcastable to shape or if the limits are outside of the range of the specified dtype.
TypeError: If the shape is not an iterable or if the dtype is an invalid numpy dtype.

Methods

`eq`

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__eq__(
    other
)

Checks if the shape and dtype of two specs are equal.

`ne`

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__ne__(
    other
)

Return self!=value.

`check_array`

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check_array(
    array
)

Return true if the given array conforms to the spec.

`from_array`

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@staticmethod
from_array(
    array, name=None
)

Construct a spec from the given array or number.

`from_spec`

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@classmethod
from_spec(
    cls, spec, name=None
)

Construct a spec from the given spec.