diff --git a/metastable_baselines/distributions/distributions.py b/metastable_baselines/distributions/distributions.py
index d656542..22dc522 100644
--- a/metastable_baselines/distributions/distributions.py
+++ b/metastable_baselines/distributions/distributions.py
@@ -136,7 +136,7 @@ class UniversalGaussianDistribution(SB3_Distribution):
     :param action_dim:  Dimension of the action space.
     """
 
-    def __init__(self, action_dim: int, use_sde: bool = False, neural_strength: Strength = Strength.DIAG, cov_strength: Strength = Strength.DIAG, parameterization_type: ParametrizationType = ParametrizationType.NONE, enforce_positive_type: EnforcePositiveType = EnforcePositiveType.ABS, prob_squashing_type: ProbSquashingType = ProbSquashingType.NONE, epsilon=1e-3, sde_learn_features=False, sde_latent_softmax=False):
+    def __init__(self, action_dim: int, use_sde: bool = False, neural_strength: Strength = Strength.DIAG, cov_strength: Strength = Strength.DIAG, parameterization_type: ParametrizationType = ParametrizationType.NONE, enforce_positive_type: EnforcePositiveType = EnforcePositiveType.ABS, prob_squashing_type: ProbSquashingType = ProbSquashingType.NONE, epsilon=1e-3, sde_learn_features=False, sde_latent_softmax=False, use_hybrid=False, hybrid_rex_fac=0.5):
         super(UniversalGaussianDistribution, self).__init__()
         self.action_dim = action_dim
         self.par_strength = cast_to_enum(neural_strength, Strength)
@@ -154,9 +154,15 @@ class UniversalGaussianDistribution(SB3_Distribution):
         self.gaussian_actions = None
 
         self.use_sde = use_sde
+        self.use_hybrid = use_hybrid
+        self.hybrid_rex_fac = hybrid_rex_fac
         self.learn_features = sde_learn_features
         self.sde_latent_softmax = sde_latent_softmax
 
+        if self.use_hybrid and not self.use_sde:
+            print('[!] use_hybrid forces use_sde to be true')
+            self.use_sde = True
+
         assert (self.par_type != ParametrizationType.NONE) == (
             self.cov_strength == Strength.FULL), 'You should set an ParameterizationType iff the cov-strength is full'
 
@@ -298,7 +304,9 @@ class UniversalGaussianDistribution(SB3_Distribution):
         return self.distribution.entropy()
 
     def sample(self) -> th.Tensor:
-        if self.use_sde:
+        if self.use_hybrid:
+            return self._sample_hybrid()
+        elif self.use_sde:
             return self._sample_sde()
         else:
             return self._sample_normal()
@@ -315,6 +323,11 @@ class UniversalGaussianDistribution(SB3_Distribution):
         self.gaussian_actions = actions
         return self.prob_squashing_type.apply(actions)
 
+    def _sample_hybrid(self) -> th.Tensor:
+        f = self.hybrid_rex_factor
+        actions = self._sample_normal()*f + self._sample_sde()*(1-f)
+        return self.prob_squashing_type.apply(actions)
+
     def mode(self) -> th.Tensor:
         mode = self.distribution.mean
         self.gaussian_actions = mode