def sample_dist_from_space(space: gym.Space,
seed: int = 0) -> Iterator[SampleDist]:
"""Creates function to sample `n` elements from from `space`."""
space.seed(seed)
def f(n: int) -> np.ndarray:
return np.array([space.sample() for _ in range(n)])
yield f
def test_seeding_works(base_space: gym.Space):
sparse_space = Sparse(base_space, sparsity=0.)
base_space.seed(123)
base_sample = base_space.sample()
sparse_space.seed(123)
sparse_sample = sparse_space.sample()
assert equals(base_sample, sparse_sample)
def test_flatten(base_space: gym.Space):
sparse_space = Sparse(base_space, sparsity=0.)
base_space.seed(123)
base_sample = base_space.sample()
flattened_base_sample = flatten(base_space, base_sample)
sparse_space.seed(123)
sparse_sample = sparse_space.sample()
flattened_sparse_sample = flatten(sparse_space, sparse_sample)
assert equals(flattened_base_sample, flattened_sparse_sample)
