rlberry.spaces.MultiBinary

class rlberry.spaces.MultiBinary(n)[source]

Bases: MultiBinary

Inherited from gymnasium.spaces.MultiBinary for compatibility with gym.

rlberry wraps gym.spaces to make sure the seeding mechanism is unified in the library (rlberry.seeding)

Attributes:
rngnumpy.random._generator.Generator

random number generator provided by rlberry.seeding

Methods

reseed()

get new random number generator
contains(x: Any) → bool[source]

Return boolean specifying if x is a valid member of this space.

from_jsonable(sample_n: list[Sequence[int]]) → list[numpy.ndarray[Any, numpy.dtype[numpy.int8]]][source]

Convert a JSONable data type to a batch of samples from this space.

property is_np_flattenable

Checks whether this space can be flattened to a spaces.Box.

property np_random: 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.

reseed(seed_seq=None)[source]

Get new random number generator.

Parameters:
seed_seqnp.random.SeedSequence, rlberry.seeding.Seeder or int, defaultNone

Seed sequence from which to spawn the random number generator. If None, generate random seed. If int, use as entropy for SeedSequence. If seeder, use seeder.seed_seq

sample()[source]

Generates a single random sample from this space.

A sample is drawn by independent, fair coin tosses (one toss per binary variable of the space).

Args:
mask: An optional np.ndarray to mask samples with expected shape of space.shape.

For mask == 0 then the samples will be 0 and mask == 1` then random samples will be generated. The expected mask shape is the space shape and mask dtype is ``np.int8.

Returns:

Sampled values from space

seed(seed: int | None = None) → int | list[int] | dict[str, int]

Seed the pseudorandom number generator (PRNG) of this space and, if applicable, the PRNGs of subspaces.

Args:

seed: The seed value for the space. This is expanded for composite spaces to accept multiple values. For further details, please refer to the space’s documentation.

Returns:

The seed values used for all the PRNGs, for composite spaces this can be a tuple or dictionary of values.

property shape: tuple[int, ...]

Has stricter type than gym.Space - never None.

to_jsonable(sample_n: Sequence[ndarray[Any, dtype[int8]]]) → list[Sequence[int]][source]

Convert a batch of samples from this space to a JSONable data type.