245 lines
9.3 KiB
Python
245 lines
9.3 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Union
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from enum import Enum
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import torch as th
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from torch import nn
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from torch.distributions import Normal, MultivariateNormal
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.distributions import sum_independent_dims
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from stable_baselines3.common.distributions import Distribution as SB3_Distribution
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from stable_baselines3.common.distributions import DiagGaussianDistribution
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from ..misc.fakeModule import FakeModule
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from ..misc.distTools import new_dist_like
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# TODO: Integrate and Test what I currently have before adding more complexity
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# TODO: Support Squashed Dists (tanh)
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# TODO: Contextual Cov
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# TODO: - Hybrid
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# TODO: Contextual SDE (Scalar + Diag + Full)
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# TODO: (SqrtInducedCov (Scalar + Diag + Full))
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class Strength(Enum):
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NONE = 0
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SCALAR = 1
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DIAG = 2
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FULL = 3
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# def __init__(self, num):
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# self.num = num
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# @property
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# def foo(self):
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# return self.num
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class ParametrizationType(Enum):
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CHOL = 1
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SPHERICAL_CHOL = 2
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GIVENS = 3
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class EnforcePositiveType(Enum):
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SOFTPLUS = 1
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ABS = 2
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RELU = 3
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SELU = 4
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LOG = 5
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class ProbSquashingType(Enum):
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NONE = 0
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TANH = 1
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def get_legal_setups(allowedEPTs=None, allowedParStrength=None, allowedCovStrength=None, allowedPTs=None, allowedPSTs=None):
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allowedEPTs = allowedEPTs or EnforcePositiveType
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allowedParStrength = allowedParStrength or Strength
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allowedCovStrength = allowedCovStrength or Strength
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allowedPTs = allowedPTs or ParametrizationType
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allowedPSTs = allowedPSTs or ProbSquashingType
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for ps in allowedParStrength:
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for cs in allowedCovStrength:
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if ps.value > cs.value:
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continue
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if ps == Strength.SCALAR and cs == Strength.FULL:
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# TODO: Maybe allow?
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continue
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if ps == Strength.NONE:
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yield (ps, cs, None, None)
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else:
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for ept in allowedEPTs:
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if cs == Strength.FULL:
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for pt in allowedPTs:
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yield (ps, cs, ept, pt)
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else:
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yield (ps, cs, ept, None)
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class UniversalGaussianDistribution(SB3_Distribution):
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"""
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Gaussian distribution with configurable covariance matrix shape and optional contextual parametrization mechanism, for continuous actions.
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:param action_dim: Dimension of the action space.
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"""
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def __init__(self, action_dim: int):
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super(UniversalGaussianDistribution, self).__init__()
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self.par_strength = Strength.DIAG
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self.cov_strength = Strength.DIAG
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self.par_type = ParametrizationType.CHOL
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self.enforce_positive_type = EnforcePositiveType.LOG
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self.prob_squashing_type = ProbSquashingType.TANH
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self.distribution = None
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def new_dist_like_me(self, mean, pseudo_chol):
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p = self.distribution
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np = new_dist_like(p, mean, pseudo_chol)
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new = UniversalGaussianDistribution(self.action_dim)
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new.par_strength = self.par_strength
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new.cov_strength = self.cov_strength
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new.par_type = self.par_type
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new.enforce_positive_type = self.enforce_positive_type
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new.prob_squashing_type = self.prob_squashing_type
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new.distribution = np
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return new
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def proba_distribution_net(self, latent_dim: int, log_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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one output will be the mean of the Gaussian, the other parameter will be the
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standard deviation (log std in fact to allow negative values)
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:param latent_dim: Dimension of the last layer of the policy (before the action layer)
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:param log_std_init: Initial value for the log standard deviation
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:return: We return two nn.Modules (mean, pseudo_chol). chol can return a díagonal vector when it would only be a diagonal matrix.
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"""
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# TODO: Rename pseudo_cov to pseudo_chol
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mean_actions = nn.Linear(latent_dim, self.action_dim)
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if self.par_strength == Strength.NONE:
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if self.cov_strength == Strength.NONE:
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pseudo_cov_par = th.ones(self.action_dim) * log_std_init
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elif self.cov_strength == Strength.SCALAR:
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pseudo_cov_par = th.ones(self.action_dim) * \
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nn.Parameter(log_std_init, requires_grad=True)
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elif self.cov_strength == Strength.DIAG:
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pseudo_cov_par = nn.Parameter(
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th.ones(self.action_dim) * log_std_init, requires_grad=True)
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elif self.cov_strength == Strength.FULL:
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# TODO: This won't work, need to ensure SPD!
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# TODO: Off-axis init?
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pseudo_cov_par = nn.Parameter(
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th.diag_embed(th.ones(self.action_dim) * log_std_init), requires_grad=True)
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pseudo_cov = FakeModule(pseudo_cov_par)
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elif self.par_strength == self.cov_strength:
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if self.par_strength == Strength.NONE:
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pseudo_cov = FakeModule(th.ones(self.action_dim))
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elif self.par_strength == Strength.SCALAR:
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# TODO: Does it work like this? Test!
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std = nn.Linear(latent_dim, 1)
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pseudo_cov = th.ones(self.action_dim) * std
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elif self.par_strength == Strength.DIAG:
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pseudo_cov = nn.Linear(latent_dim, self.action_dim)
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elif self.par_strength == Strength.FULL:
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pseudo_cov = self._parameterize_full(latent_dim)
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elif self.par_strength > self.cov_strength:
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raise Exception(
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'The parameterization can not be stronger than the actual covariance.')
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else:
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if self.par_strength == Strength.SCALAR and self.cov_strength == Strength.DIAG:
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pseudo_cov = self._parameterize_hybrid_from_scalar(latent_dim)
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elif self.par_strength == Strength.DIAG and self.cov_strength == Strength.FULL:
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pseudo_cov = self._parameterize_hybrid_from_diag(latent_dim)
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elif self.par_strength == Strength.SCALAR and self.cov_strength == Strength.FULL:
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raise Exception(
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'That does not even make any sense...')
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else:
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raise Exception("This Exception can't happen (I think)")
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return mean_actions, pseudo_cov
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def _parameterize_full(self, latent_dim):
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# TODO: Implement various techniques for full parameterization (forcing SPD)
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raise Exception(
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'Programmer-was-to-lazy-to-implement-this-Exception')
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def _parameterize_hybrid_from_diag(self, latent_dim):
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# TODO: Implement the hybrid-method for DIAG -> FULL (parameters for pearson-correlation-matrix)
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raise Exception(
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'Programmer-was-to-lazy-to-implement-this-Exception')
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def _parameterize_hybrid_from_scalar(self, latent_dim):
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factor = nn.Linear(latent_dim, 1)
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par_cov = th.ones(self.action_dim) * \
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nn.Parameter(1, requires_grad=True)
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pseudo_cov = par_cov * factor[0]
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return pseudo_cov
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def proba_distribution(self, mean_actions: th.Tensor, pseudo_cov: th.Tensor) -> "UniversalGaussianDistribution":
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"""
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Create the distribution given its parameters (mean, pseudo_cov)
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:param mean_actions:
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:param pseudo_cov:
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:return:
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"""
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action_std = None
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# TODO: Needs to be expanded
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if self.cov_strength == Strength.DIAG:
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if self.enforce_positive_type == EnforcePositiveType.LOG:
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action_std = pseudo_cov.exp()
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if action_std == None:
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raise Exception('Not yet implemented!')
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self.distribution = Normal(mean_actions, action_std)
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if self.distribution == None:
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raise Exception('Not yet implemented!')
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return self
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def log_prob(self, actions: th.Tensor) -> th.Tensor:
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"""
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Get the log probabilities of actions according to the distribution.
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Note that you must first call the ``proba_distribution()`` method.
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:param actions:
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:return:
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"""
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log_prob = self.distribution.log_prob(actions)
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return sum_independent_dims(log_prob)
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def entropy(self) -> th.Tensor:
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return sum_independent_dims(self.distribution.entropy())
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def sample(self) -> th.Tensor:
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# Reparametrization trick to pass gradients
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return self.distribution.rsample()
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def mode(self) -> th.Tensor:
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return self.distribution.mean
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def actions_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor, deterministic: bool = False) -> th.Tensor:
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# Update the proba distribution
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self.proba_distribution(mean_actions, log_std)
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return self.get_actions(deterministic=deterministic)
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def log_prob_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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"""
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Compute the log probability of taking an action
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given the distribution parameters.
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:param mean_actions:
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:param log_std:
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:return:
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"""
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actions = self.actions_from_params(mean_actions, log_std)
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log_prob = self.log_prob(actions)
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return actions, log_prob
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