Working on SDC

metastable_baselines/distributions/distributions.py

@@ -28,31 +28,39 @@ class Strength(Enum):
     DIAG = 2
     FULL = 3
 
-    # def __init__(self, num):
-    #    self.num = num
-
-    # @property
-    # def foo(self):
-    #    return self.num
-
 
 class ParametrizationType(Enum):
+    # Currently only Chol is implemented
     CHOL = 1
-    SPHERICAL_CHOL = 2
+    #SPHERICAL_CHOL = 2
-    GIVENS = 3
+    #GIVENS = 3
 
 
 class EnforcePositiveType(Enum):
-    SOFTPLUS = 1
+    # TODO: Allow custom params for softplus?
-    ABS = 2
+    SOFTPLUS = (1, nn.Softplus(beta=1, threshold=20))
-    RELU = 3
+    ABS = (2, th.abs)
-    SELU = 4
+    RELU = (3, nn.ReLU(inplace=False))
-    LOG = 5
+    LOG = (4, th.log)
+
+    def __init__(self, value, func):
+        self.value = value
+        self._func = func
+
+    def apply(self, x):
+        return self._func(x)
 
 
 class ProbSquashingType(Enum):
-    NONE = 0
+    NONE = (0, nn.Identity())
-    TANH = 1
+    TANH = (1, th.tanh)
+
+    def __init__(self, value, func):
+        self.value = value
+        self._func = func
+
+    def apply(self, x):
+        return self._func(x)
 
 
 def get_legal_setups(allowedEPTs=None, allowedParStrength=None, allowedCovStrength=None, allowedPTs=None, allowedPSTs=None):
@@ -118,10 +126,10 @@ class UniversalGaussianDistribution(SB3_Distribution):
 
         :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.
+        :return: We return two nn.Modules (mean, chol).
         """
 
-        # TODO: Rename pseudo_cov to pseudo_chol
+        # TODO: Allow chol to be vector when only diagonal.
 
         mean_actions = nn.Linear(latent_dim, self.action_dim)
 
@@ -139,33 +147,33 @@ class UniversalGaussianDistribution(SB3_Distribution):
                 # 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)
+            chol = 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))
+                chol = 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
+                chol = th.ones(self.action_dim) * std
             elif self.par_strength == Strength.DIAG:
-                pseudo_cov = nn.Linear(latent_dim, self.action_dim)
+                chol = nn.Linear(latent_dim, self.action_dim)
             elif self.par_strength == Strength.FULL:
-                pseudo_cov = self._parameterize_full(latent_dim)
+                chol = 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)
+                chol = 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)
+                chol = 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
+        return mean_actions, chol
 
     def _parameterize_full(self, latent_dim):
         # TODO: Implement various techniques for full parameterization (forcing SPD)
@@ -177,6 +185,13 @@ class UniversalGaussianDistribution(SB3_Distribution):
         raise Exception(
             'Programmer-was-to-lazy-to-implement-this-Exception')
 
+    def _ensure_positive_func(self, x):
+        return self.enforce_positive_type.apply(x)
+
+    def _ensure_diagonal_positive(self, pseudo_chol):
+        pseudo_chol.tril(-1) + self._ensure_positive_func(pseudo_chol.diagonal(dim1=-2,
+                                                                               dim2=-1)).diag_embed() + pseudo_chol.triu(1)
+
     def _parameterize_hybrid_from_scalar(self, latent_dim):
         factor = nn.Linear(latent_dim, 1)
         par_cov = th.ones(self.action_dim) * \