Fixing bugs with w2 and sqrt_induced_gaussian
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@@ -184,6 +184,16 @@ class UniversalGaussianDistribution(SB3_Distribution):
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return new
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def new_dist_like_me_from_sqrt(self, mean: th.Tensor, cov_sqrt: th.Tensor):
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chol = self._sqrt_to_chol(cov_sqrt)
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new = self.new_dist_like_me(mean, chol)
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new.cov_sqrt = cov_sqrt
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new.distribution.cov_sqrt = cov_sqrt
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return new
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def proba_distribution_net(self, latent_dim: int, latent_sde_dim: int, std_init: float = 0.0) -> Tuple[nn.Module, nn.Module]:
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"""
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Create the layers and parameter that represent the distribution:
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@@ -206,6 +216,22 @@ class UniversalGaussianDistribution(SB3_Distribution):
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return mean_actions, chol
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def _sqrt_to_chol(self, cov_sqrt):
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vec = False
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if len(cov_sqrt.shape) == 2:
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vec = True
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if vec:
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cov_sqrt = th.diag_embed(cov_sqrt)
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cov = th.bmm(cov_sqrt.mT, cov_sqrt)
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chol = th.linalg.cholesky(cov)
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if vec:
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chol = th.diagonal(chol, dim1=-2, dim2=-1)
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return chol
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def proba_distribution_from_sqrt(self, mean_actions: th.Tensor, cov_sqrt: th.Tensor, latent_pi: nn.Module) -> "UniversalGaussianDistribution":
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"""
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Create the distribution given its parameters (mean, cov_sqrt)
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@@ -214,12 +240,11 @@ class UniversalGaussianDistribution(SB3_Distribution):
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:param cov_sqrt:
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:return:
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"""
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cov = cov_sqrt.T @ cov_sqrt
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chol = th.linalg.cholesky(cov)
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self.cov_sqrt = cov_sqrt
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return self.proba_distribution(mean_actions, chol, latent_pi)
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chol = self._sqrt_to_chol(cov_sqrt)
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self.proba_distribution(mean_actions, chol, latent_pi)
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self.distribution.cov_sqrt = cov_sqrt
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return self
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def proba_distribution(self, mean_actions: th.Tensor, chol: th.Tensor, latent_pi: nn.Module) -> "UniversalGaussianDistribution":
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"""
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