Delta

class torchrl.modules.Delta(param: Tensor, atol: float = 1e-06, rtol: float = 1e-06, batch_shape: Optional[Union[Size, Sequence[int]]] = None, event_shape: Optional[Union[Size, Sequence[int]]] = None)

Delta distribution.

Parameters:

• param (torch.Tensor) – parameter of the delta distribution;

• atol (number, optional) – absolute tolerance to consider that a tensor matches the distribution parameter; Default is 1e-6

• rtol (number, optional) – relative tolerance to consider that a tensor matches the distribution parameter; Default is 1e-6

• batch_shape (torch.Size, optional) – batch shape;

• event_shape (torch.Size, optional) – shape of the outcome.

expand(batch_shape: Size, _instance=None)

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created.

Parameters:

• batch_shape (torch.Size) – the desired expanded size.

• _instance – new instance provided by subclasses that need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to batch_size.

log_prob(value: Tensor) → Tensor

Returns the log of the probability density/mass function evaluated at value.

Parameters:

value (Tensor) –

property mean: Tensor

Returns the mean of the distribution.

property mode: Tensor

Returns the mode of the distribution.

rsample(size=None) → Tensor

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample(size=None) → Tensor

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.