ray.rllib.utils.numpy.flatten_inputs_to_1d_tensor#

ray.rllib.utils.numpy.flatten_inputs_to_1d_tensor(inputs: numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor | dict | tuple, spaces_struct: gymnasium.spaces.Space | dict | tuple | None = None, time_axis: bool = False) numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor[source]#

Flattens arbitrary input structs according to the given spaces struct.

Returns a single 1D tensor resulting from the different input components’ values.

Thereby: - Boxes (any shape) get flattened to (B, [T]?, -1). Note that image boxes are not treated differently from other types of Boxes and get flattened as well. - Discrete (int) values are one-hot’d, e.g. a batch of [1, 0, 3] (B=3 with Discrete(4) space) results in [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]]. - MultiDiscrete values are multi-one-hot’d, e.g. a batch of [[0, 2], [1, 4]] (B=2 with MultiDiscrete([2, 5]) space) results in [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1]].

Parameters:
  • inputs – The inputs to be flattened.

  • spaces_struct – The (possibly nested) structure of the spaces that inputs belongs to.

  • time_axis – Whether all inputs have a time-axis (after the batch axis). If True, will keep not only the batch axis (0th), but the time axis (1st) as-is and flatten everything from the 2nd axis up.

Returns:

A single 1D tensor resulting from concatenating all flattened/one-hot’d input components. Depending on the time_axis flag, the shape is (B, n) or (B, T, n).

# B=2
from ray.rllib.utils.tf_utils import flatten_inputs_to_1d_tensor
from gymnasium.spaces import Discrete, Box
out = flatten_inputs_to_1d_tensor(
    {"a": [1, 0], "b": [[[0.0], [0.1]], [1.0], [1.1]]},
    spaces_struct=dict(a=Discrete(2), b=Box(shape=(2, 1)))
)
print(out)

# B=2; T=2
out = flatten_inputs_to_1d_tensor(
    ([[1, 0], [0, 1]],
     [[[0.0, 0.1], [1.0, 1.1]], [[2.0, 2.1], [3.0, 3.1]]]),
    spaces_struct=tuple([Discrete(2), Box(shape=(2, ))]),
    time_axis=True
)
print(out)
[[0.0, 1.0,  0.0, 0.1], [1.0, 0.0,  1.0, 1.1]]  # B=2 n=4
[[[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]],
[[1.0, 0.0, 2.0, 2.1], [0.0, 1.0, 3.0, 3.1]]]  # B=2 T=2 n=4