MaskedOneHotCategorical

class torchrl.modules.MaskedOneHotCategorical(logits: Optional[Tensor] = None, probs: Optional[Tensor] = None, mask: Optional[Tensor] = None, indices: Optional[Tensor] = None, neg_inf: float = - inf, padding_value: Optional[int] = None, grad_method: ReparamGradientStrategy = ReparamGradientStrategy.PassThrough)

MaskedCategorical distribution.

Reference: https://www.tensorflow.org/agents/api_docs/python/tf_agents/distributions/masked/MaskedCategorical

Parameters:

• logits (torch.Tensor) – event log probabilities (unnormalized)

• probs (torch.Tensor) – event probabilities. If provided, the probabilities corresponding to to masked items will be zeroed and the probability re-normalized along its last dimension.

Keyword Arguments:

• mask (torch.Tensor) – A boolean mask of the same shape as logits/probs where False entries are the ones to be masked. Alternatively, if sparse_mask is True, it represents the list of valid indices in the distribution. Exclusive with indices.

• indices (torch.Tensor) – A dense index tensor representing which actions must be taken into account. Exclusive with mask.

• neg_inf (float, optional) – The log-probability value allocated to invalid (out-of-mask) indices. Defaults to -inf.

• padding_value – The padding value in then mask tensor when sparse_mask == True, the padding_value will be ignored.

• grad_method (ReparamGradientStrategy, optional) –

  strategy to gather reparameterized samples. ReparamGradientStrategy.PassThrough will compute the sample gradients

  by using the softmax valued log-probability as a proxy to the samples gradients.

  ReparamGradientStrategy.RelaxedOneHot will use torch.distributions.RelaxedOneHot to sample from the distribution.

• torch.manual_seed (>>>) –

• torch.randn (>>> logits =) –

• torch.tensor (>>> mask =) –

• MaskedOneHotCategorical (>>> dist =) –

• dist.sample (>>> sample =) –

• print (>>>) –

• 0], (tensor([[0, 0, 1,) – [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]])

• print –

• -1.0831, (tensor([-1.1203, -1.0928, -1.0831, -1.1203, -1.1203, -1.0831, -1.1203,) – -1.1203, -1.1203])

• torch.zeros_like (>>> sample_non_valid =) –

• 1 (>>> sample_non_valid[..., 1] =) –

• print –

• tensor ([-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf]) –

• probabilities (>>> # with) –

• torch.ones (>>> prob =) –

• prob.sum() (>>> prob = prob /) –

• torch.tensor –

• MaskedOneHotCategorical –

• torch.arange (>>> s =) –

• torch.nn.functional.one_hot (>>> s =) –

• print –

• -2.1972, (tensor([ -inf, -2.1972, -2.1972, -2.1972, -2.1972, -2.1972, -2.1972,) – -2.1972, -2.1972])

log_prob(value: Tensor) → Tensor

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

Parameters:

value (Tensor) –

rsample(sample_shape: Optional[Union[Size, Sequence]] = None) → Tensor

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

sample(sample_shape: Optional[Union[Size, Sequence[int]]] = None) → Tensor

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