diff --git a/sb3_trl/misc/distTools.py b/sb3_trl/misc/distTools.py
index e7ee3e0..6496ea8 100644
--- a/sb3_trl/misc/distTools.py
+++ b/sb3_trl/misc/distTools.py
@@ -12,7 +12,7 @@ def get_mean_and_chol(p, expand=False):
     elif isinstance(p, th.distributions.MultivariateNormal):
         return p.mean, p.scale_tril
     elif isinstance(p, SB3_Distribution):
-        return get_mean_and_chol(p.distribution)
+        return get_mean_and_chol(p.distribution, expand=expand)
     else:
         raise Exception('Dist-Type not implemented')
 
@@ -28,6 +28,24 @@ def get_cov(p):
         raise Exception('Dist-Type not implemented')
 
 
+def has_diag_cov(p, numerical_check=True):
+    if isinstance(p, SB3_Distribution):
+        return has_diag_cov(p.distribution, numerical_check=numerical_check)
+    if isinstance(p, th.distributions.Normal):
+        return True
+    if not numerical_check:
+        return False
+    # Check if matrix is diag
+    cov = get_cov(p)
+    return th.equal(cov - th.diag_embed(th.diagonal(cov, dim1=-2, dim2=-1), th.zeros_like(cov)))
+
+
+def get_diag_cov_vec(p, check_diag=True, numerical_check=True):
+    if check_diag and not has_diag_cov(p):
+        raise Exception('Cannot reduce cov-mat to diag-vec: Is not diagonal')
+    return th.diagonal(get_cov(p), dim1=-2, dim2=-1)
+
+
 def new_dist_like(orig_p, mean, chol):
     if isinstance(orig_p, th.distributions.Normal):
         if orig_p.stddev.shape != chol.shape:
diff --git a/sb3_trl/misc/norm.py b/sb3_trl/misc/norm.py
index 5715334..74d1f66 100644
--- a/sb3_trl/misc/norm.py
+++ b/sb3_trl/misc/norm.py
@@ -2,7 +2,7 @@ import torch as th
 from torch.distributions.multivariate_normal import _batch_mahalanobis
 
 
-def mahalanobis_blub(u, v, std):
+def mahalanobis_alt(u, v, std):
     delta = u - v
     return th.triangular_solve(delta, std, upper=False)[0].pow(2).sum([-2, -1])
 
@@ -13,7 +13,7 @@ def mahalanobis(u, v, cov):
 
 
 def frob_sq(diff, is_spd=False):
-    # If diff is spd, we can use a more perfromant algorithm
+    # If diff is spd, we can use a (probably) more performant algorithm
     if is_spd:
         return _frob_sq_spd(diff)
     return th.norm(diff, p='fro', dim=tuple(range(1, diff.dim()))).pow(2)