202 lines
7.6 KiB
Python
202 lines
7.6 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Union
|
|
from enum import Enum
|
|
|
|
import torch as th
|
|
from torch import nn
|
|
from torch.distributions import Normal, MultivariateNormal
|
|
|
|
from stable_baselines3.common.preprocessing import get_action_dim
|
|
|
|
from stable_baselines3.common.distributions import sum_independent_dims
|
|
from stable_baselines3.common.distributions import Distribution as SB3_Distribution
|
|
from stable_baselines3.common.distributions import DiagGaussianDistribution
|
|
|
|
from ..misc.fakeModule import FakeModule
|
|
|
|
# TODO: Full Cov Parameter
|
|
# TODO: Contextual Cov
|
|
# TODO: - Scalar
|
|
# TODO: - Diag
|
|
# TODO: - Full
|
|
# TODO: - Hybrid
|
|
# TODO: Contextual SDE (Scalar + Diag + Full)
|
|
# TODO: (SqrtInducedCov (Scalar + Diag + Full))
|
|
# TODO: (Support Squased Dists (tanh))
|
|
|
|
|
|
class Strength(Enum):
|
|
NONE = 0
|
|
SCALAR = 1
|
|
DIAG = 2
|
|
FULL = 3
|
|
|
|
def __init__(self, num):
|
|
self.num = num
|
|
|
|
@property
|
|
def foo(self):
|
|
return self.num
|
|
|
|
|
|
class ParametrizationType(Enum):
|
|
CHOL = 1
|
|
ARCHAKOVA = 2
|
|
|
|
|
|
class EnforcePositiveType(Enum):
|
|
LOG = 1
|
|
RELU = 2
|
|
SELU = 3
|
|
ABS = 4
|
|
SQ = 5
|
|
|
|
|
|
class UniversalGaussianDistribution(SB3_Distribution):
|
|
"""
|
|
Gaussian distribution with configurable covariance matrix shape and optional contextual parametrization mechanism, for continuous actions.
|
|
|
|
:param action_dim: Dimension of the action space.
|
|
"""
|
|
|
|
def __init__(self, action_dim: int):
|
|
super(UniversalGaussianDistribution, self).__init__()
|
|
self.par_strength = Strength.DIAG
|
|
self.cov_strength = Strength.DIAG
|
|
self.par_type = ParametrizationType.CHOL
|
|
self.enforce_positive_type = EnforcePositiveType.LOG
|
|
|
|
self.distribution = None
|
|
|
|
def proba_distribution_net(self, latent_dim: int, log_std_init: float = 0.0) -> Tuple[nn.Module, nn.Module]:
|
|
"""
|
|
Create the layers and parameter that represent the distribution:
|
|
one output will be the mean of the Gaussian, the other parameter will be the
|
|
standard deviation (log std in fact to allow negative values)
|
|
|
|
:param latent_dim: Dimension of the last layer of the policy (before the action layer)
|
|
:param log_std_init: Initial value for the log standard deviation
|
|
:return: We return two nn.Modules (mean, pseudo_chol). chol can return a díagonal vector when it would only be a diagonal matrix.
|
|
"""
|
|
|
|
# TODO: Rename pseudo_cov to pseudo_chol
|
|
|
|
mean_actions = nn.Linear(latent_dim, self.action_dim)
|
|
|
|
if self.par_strength == Strength.NONE:
|
|
if self.cov_strength == Strength.NONE:
|
|
pseudo_cov_par = th.ones(self.action_dim) * log_std_init
|
|
elif self.cov_strength == Strength.SCALAR:
|
|
pseudo_cov_par = th.ones(self.action_dim) * \
|
|
nn.Parameter(log_std_init, requires_grad=True)
|
|
elif self.cov_strength == Strength.DIAG:
|
|
pseudo_cov_par = nn.Parameter(
|
|
th.ones(self.action_dim) * log_std_init, requires_grad=True)
|
|
elif self.cov_strength == Strength.FULL:
|
|
# TODO: This won't work, need to ensure SPD!
|
|
# TODO: Off-axis init?
|
|
pseudo_cov_par = nn.Parameter(
|
|
th.diag_embed(th.ones(self.action_dim) * log_std_init), requires_grad=True)
|
|
pseudo_cov = FakeModule(pseudo_cov_par)
|
|
elif self.par_strength == self.cov_strength:
|
|
if self.par_strength == Strength.NONE:
|
|
pseudo_cov = FakeModule(th.ones(self.action_dim))
|
|
elif self.par_strength == Strength.SCALAR:
|
|
# TODO: Does it work like this? Test!
|
|
std = nn.Linear(latent_dim, 1)
|
|
pseudo_cov = th.ones(self.action_dim) * std
|
|
elif self.par_strength == Strength.DIAG:
|
|
pseudo_cov = nn.Linear(latent_dim, self.action_dim)
|
|
elif self.par_strength == Strength.FULL:
|
|
pseudo_cov = self._parameterize_full(latent_dim)
|
|
elif self.par_strength > self.cov_strength:
|
|
raise Exception(
|
|
'The parameterization can not be stronger than the actual covariance.')
|
|
else:
|
|
if self.par_strength == Strength.SCALAR and self.cov_strength == Strength.DIAG:
|
|
pseudo_cov = self._parameterize_hybrid_from_scalar(latent_dim)
|
|
elif self.par_strength == Strength.DIAG and self.cov_strength == Strength.FULL:
|
|
pseudo_cov = self._parameterize_hybrid_from_diag(latent_dim)
|
|
elif self.par_strength == Strength.SCALAR and self.cov_strength == Strength.FULL:
|
|
raise Exception(
|
|
'That does not even make any sense...')
|
|
else:
|
|
raise Exception("This Exception can't happen (I think)")
|
|
|
|
return mean_actions, pseudo_cov
|
|
|
|
def _parameterize_full(self, latent_dim):
|
|
# TODO: Implement various techniques for full parameterization (forcing SPD)
|
|
raise Exception(
|
|
'Programmer-was-to-lazy-to-implement-this-Exception')
|
|
|
|
def _parameterize_hybrid_from_diag(self, latent_dim):
|
|
# TODO: Implement the hybrid-method for DIAG -> FULL (parameters for pearson-correlation-matrix)
|
|
raise Exception(
|
|
'Programmer-was-to-lazy-to-implement-this-Exception')
|
|
|
|
def _parameterize_hybrid_from_scalar(self, latent_dim):
|
|
factor = nn.Linear(latent_dim, 1)
|
|
par_cov = th.ones(self.action_dim) * \
|
|
nn.Parameter(1, requires_grad=True)
|
|
pseudo_cov = par_cov * factor[0]
|
|
return pseudo_cov
|
|
|
|
def proba_distribution(self, mean_actions: th.Tensor, pseudo_cov: th.Tensor) -> "UniversalGaussianDistribution":
|
|
"""
|
|
Create the distribution given its parameters (mean, pseudo_cov)
|
|
|
|
:param mean_actions:
|
|
:param pseudo_cov:
|
|
:return:
|
|
"""
|
|
action_std = None
|
|
# TODO: Needs to be expanded
|
|
if self.cov_strength == Strength.DIAG:
|
|
if self.enforce_positive_type == EnforcePositiveType.LOG:
|
|
action_std = pseudo_cov.exp()
|
|
if action_std == None:
|
|
raise Exception('Not yet implemented!')
|
|
self.distribution = Normal(mean_actions, action_std)
|
|
if self.distribution == None:
|
|
raise Exception('Not yet implemented!')
|
|
return self
|
|
|
|
def log_prob(self, actions: th.Tensor) -> th.Tensor:
|
|
"""
|
|
Get the log probabilities of actions according to the distribution.
|
|
Note that you must first call the ``proba_distribution()`` method.
|
|
|
|
:param actions:
|
|
:return:
|
|
"""
|
|
log_prob = self.distribution.log_prob(actions)
|
|
return sum_independent_dims(log_prob)
|
|
|
|
def entropy(self) -> th.Tensor:
|
|
return sum_independent_dims(self.distribution.entropy())
|
|
|
|
def sample(self) -> th.Tensor:
|
|
# Reparametrization trick to pass gradients
|
|
return self.distribution.rsample()
|
|
|
|
def mode(self) -> th.Tensor:
|
|
return self.distribution.mean
|
|
|
|
def actions_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor, deterministic: bool = False) -> th.Tensor:
|
|
# Update the proba distribution
|
|
self.proba_distribution(mean_actions, log_std)
|
|
return self.get_actions(deterministic=deterministic)
|
|
|
|
def log_prob_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
|
|
"""
|
|
Compute the log probability of taking an action
|
|
given the distribution parameters.
|
|
|
|
:param mean_actions:
|
|
:param log_std:
|
|
:return:
|
|
"""
|
|
actions = self.actions_from_params(mean_actions, log_std)
|
|
log_prob = self.log_prob(actions)
|
|
return actions, log_prob
|