OneHotDiscreteTensorSpec

class torchrl.data.OneHotDiscreteTensorSpec(n: int, shape: Optional[torch.Size] = None, device: Optional[DEVICE_TYPING] = None, dtype: Optional[Union[str, torch.dtype]] = torch.bool, use_register: bool = False, mask: torch.Tensor | None = None)

A unidimensional, one-hot discrete tensor spec.

By default, TorchRL assumes that categorical variables are encoded as one-hot encodings of the variable. This allows for simple indexing of tensors, e.g.

>>> batch, size = 3, 4
>>> action_value = torch.arange(batch*size)
>>> action_value = action_value.view(batch, size).to(torch.float)
>>> action = (action_value == action_value.max(-1,
...    keepdim=True)[0]).to(torch.long)
>>> chosen_action_value = (action * action_value).sum(-1)
>>> print(chosen_action_value)
tensor([ 3.,  7., 11.])

The last dimension of the shape (variable domain) cannot be indexed.

Parameters:

• n (int) – number of possible outcomes.

• shape (torch.Size, optional) – total shape of the sampled tensors. If provided, the last dimension must match n.

• device (str, int or torch.device, optional) – device of the tensors.

• dtype (str or torch.dtype, optional) – dtype of the tensors.

• user_register (bool) – experimental feature. If True, every integer will be mapped onto a binary vector in the order in which they appear. This feature is designed for environment with no a-priori definition of the number of possible outcomes (e.g. discrete outcomes are sampled from an arbitrary set, whose elements will be mapped in a register to a series of unique one-hot binary vectors).

assert_is_in(value: Tensor) → None

Asserts whether a tensor belongs to the box, and raises an exception otherwise.

Parameters:

value (torch.Tensor) – value to be checked.

clear_device_()

A no-op for all leaf specs (which must have a device).

encode(val: Union[ndarray, Tensor], space: Optional[DiscreteBox] = None, *, ignore_device: bool = False) → Tensor

Encodes a value given the specified spec, and return the corresponding tensor.

Parameters:

val (np.ndarray or torch.Tensor) – value to be encoded as tensor.

Keyword Arguments:

ignore_device (bool, optional) – if True, the spec device will be ignored. This is used to group tensor casting within a call to TensorDict(..., device="cuda") which is faster.

Returns:

torch.Tensor matching the required tensor specs.

expand(*shape)

Returns a new Spec with the extended shape.

Parameters:

*shape (tuple or iterable of int) – the new shape of the Spec. Must comply with the current shape: its length must be at least as long as the current shape length, and its last values must be complient too; ie they can only differ from it if the current dimension is a singleton.

flatten(start_dim, end_dim)

Flattens a tensorspec.

Check flatten() for more information on this method.

classmethod implements_for_spec(torch_function: Callable) → Callable

Register a torch function override for TensorSpec.

index(index: Union[int, Tensor, ndarray, slice, List], tensor_to_index: Tensor) → Tensor

Indexes the input tensor.

Parameters:

• index (int, torch.Tensor, slice or list) – index of the tensor

• tensor_to_index – tensor to be indexed

Returns:

indexed tensor

is_in(val: Tensor) → bool

If the value val is in the box defined by the TensorSpec, returns True, otherwise False.

Parameters:

val (torch.Tensor) – value to be checked

Returns:

boolean indicating if values belongs to the TensorSpec box

project(val: Tensor) → Tensor

If the input tensor is not in the TensorSpec box, it maps it back to it given some heuristic.

Parameters:

val (torch.Tensor) – tensor to be mapped to the box.

Returns:

a torch.Tensor belonging to the TensorSpec box.

rand(shape=None) → Tensor

Returns a random tensor in the box. The sampling will be uniform unless the box is unbounded.

Parameters:

shape (torch.Size) – shape of the random tensor

Returns:

a random tensor sampled in the TensorSpec box.

reshape(*shape)

Reshapes a tensorspec.

Check reshape() for more information on this method.

squeeze(dim=None)

Returns a new Spec with all the dimensions of size 1 removed.

When dim is given, a squeeze operation is done only in that dimension.

Parameters:

dim (int or None) – the dimension to apply the squeeze operation to

to_categorical(val: Tensor, safe: Optional[bool] = None) → Tensor

Converts a given one-hot tensor in categorical format.

Parameters:

• val (torch.Tensor, optional) – One-hot tensor to convert in categorical format.

• safe (bool) – boolean value indicating whether a check should be performed on the value against the domain of the spec. Defaults to the value of the CHECK_SPEC_ENCODE environment variable.

Returns:

The categorical tensor.

to_categorical_spec() → DiscreteTensorSpec

Converts the spec to the equivalent categorical spec.

to_numpy(val: Tensor, safe: Optional[bool] = None) → ndarray

Returns the np.ndarray correspondent of an input tensor.

Parameters:

• val (torch.Tensor) – tensor to be transformed_in to numpy.

• safe (bool) – boolean value indicating whether a check should be performed on the value against the domain of the spec. Defaults to the value of the CHECK_SPEC_ENCODE environment variable.

Returns:

a np.ndarray

type_check(value: Tensor, key: Optional[str] = None) → None

Checks the input value dtype against the TensorSpec dtype and raises an exception if they don’t match.

Parameters:

• value (torch.Tensor) – tensor whose dtype has to be checked

• key (str, optional) – if the TensorSpec has keys, the value dtype will be checked against the spec pointed by the indicated key.

unflatten(dim, sizes)

Unflattens a tensorspec.

Check unflatten() for more information on this method.

view(*shape)

Reshapes a tensorspec.

Check reshape() for more information on this method.

zero(shape=None) → Tensor

Returns a zero-filled tensor in the box.

Parameters:

shape (torch.Size) – shape of the zero-tensor

Returns:

a zero-filled tensor sampled in the TensorSpec box.