composite_entropy#
- torchrl.modules.distributions.utils.composite_entropy(distribution: CompositeDistribution, samples_mc: int = 1) Tensor | TensorDictBase[source]#
Compute component entropy without inverse-scoring Monte Carlo samples.
Analytic component entropies are used when available. Components without analytic entropy are estimated from atomic reparameterized samples.
- Parameters:
distribution (CompositeDistribution) – distribution whose component entropies are computed.
samples_mc (int, optional) – number of Monte Carlo samples used for components without analytic entropy. Defaults to
1.
- Returns:
The aggregated entropy, or a TensorDict of component entropies when composite log-probability aggregation is disabled.