91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
import torch as th
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from stable_baselines3.common.distributions import Distribution as SB3_Distribution
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from ..distributions import UniversalGaussianDistribution, AnyDistribution
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def get_mean_and_chol(p: AnyDistribution, expand=False):
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if isinstance(p, th.distributions.Normal):
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if expand:
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return p.mean, th.diag_embed(p.stddev)
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else:
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return p.mean, p.stddev
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elif isinstance(p, th.distributions.MultivariateNormal):
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return p.mean, p.scale_tril
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elif isinstance(p, SB3_Distribution):
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return get_mean_and_chol(p.distribution, expand=expand)
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else:
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raise Exception('Dist-Type not implemented')
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def get_mean_and_sqrt(p: UniversalGaussianDistribution):
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raise Exception('Not yet implemented...')
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if isinstance(p, th.distributions.Normal):
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return p.mean, p.stddev
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elif isinstance(p, th.distributions.MultivariateNormal):
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return p.mean, p.scale_tril
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elif isinstance(p, SB3_Distribution):
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return get_mean_and_chol(p.distribution)
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else:
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raise Exception('Dist-Type not implemented')
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def get_cov(p: AnyDistribution):
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if isinstance(p, th.distributions.Normal):
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return th.diag_embed(p.variance)
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elif isinstance(p, th.distributions.MultivariateNormal):
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return p.covariance_matrix
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elif isinstance(p, SB3_Distribution):
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return get_cov(p.distribution)
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else:
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raise Exception('Dist-Type not implemented')
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def has_diag_cov(p: AnyDistribution, numerical_check=True):
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if isinstance(p, SB3_Distribution):
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return has_diag_cov(p.distribution, numerical_check=numerical_check)
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if isinstance(p, th.distributions.Normal):
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return True
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if not numerical_check:
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return False
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# Check if matrix is diag
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cov = get_cov(p)
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return th.equal(cov - th.diag_embed(th.diagonal(cov, dim1=-2, dim2=-1), th.zeros_like(cov)))
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def is_contextual(p: AnyDistribution):
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# TODO: Implement for UniveralGaussianDist
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return False
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def get_diag_cov_vec(p: AnyDistribution, check_diag=True, numerical_check=True):
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if check_diag and not has_diag_cov(p):
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raise Exception('Cannot reduce cov-mat to diag-vec: Is not diagonal')
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return th.diagonal(get_cov(p), dim1=-2, dim2=-1)
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def new_dist_like(orig_p: AnyDistribution, mean: th.Tensor, chol: th.Tensor):
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if isinstance(orig_p, UniversalGaussianDistribution):
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return orig_p.new_list_like_me(mean, chol)
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elif isinstance(orig_p, th.distributions.Normal):
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if orig_p.stddev.shape != chol.shape:
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chol = th.diagonal(chol, dim1=1, dim2=2)
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return th.distributions.Normal(mean, chol)
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elif isinstance(orig_p, th.distributions.MultivariateNormal):
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return th.distributions.MultivariateNormal(mean, scale_tril=chol)
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elif isinstance(orig_p, SB3_Distribution):
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p = orig_p.distribution
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if isinstance(p, th.distributions.Normal):
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p_out = orig_p.__class__(orig_p.action_dim)
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p_out.distribution = th.distributions.Normal(mean, chol)
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elif isinstance(p, th.distributions.MultivariateNormal):
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p_out = orig_p.__class__(orig_p.action_dim)
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p_out.distribution = th.distributions.MultivariateNormal(
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mean, scale_tril=chol)
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else:
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raise Exception('Dist-Type not implemented (of sb3 dist)')
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return p_out
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else:
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raise Exception('Dist-Type not implemented')
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