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)[source]¶
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 [source]¶
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 toTensorDict(..., device="cuda")
which is faster.- Returns:
torch.Tensor matching the required tensor specs.
- expand(*shape)[source]¶
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 [source]¶
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 [source]¶
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 [source]¶
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.
- squeeze(dim=None)[source]¶
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 [source]¶
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 [source]¶
Converts the spec to the equivalent categorical spec.
- to_numpy(val: Tensor, safe: Optional[bool] = None) ndarray [source]¶
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.