diff --git a/torchy_baselines/ppo/policies.py b/torchy_baselines/ppo/policies.py
index 8c4df3f..d3b897e 100644
--- a/torchy_baselines/ppo/policies.py
+++ b/torchy_baselines/ppo/policies.py
@@ -29,12 +29,18 @@ class PPOPolicy(BasePolicy):
     :param sde_net_arch: ([int]) Network architecture for extracting features
         when using SDE. If None, the latent features from the policy will be used.
         Pass an empty list to use the states as features.
+    :param use_expln: (bool) Use `expln()` function instead of `exp()` to ensure
+        a positive standard deviation (cf paper). It allows to keep variance
+        above zero and prevent it from growing too fast. In practice, `exp()` is usually enough.
+    :param squash_output: (bool) Whether to squash the output using a tanh function,
+        this allows to ensure boundaries when using SDE.
     """
     def __init__(self, observation_space, action_space,
                  learning_rate, net_arch=None, device='cpu',
                  activation_fn=nn.Tanh, adam_epsilon=1e-5,
                  ortho_init=True, use_sde=False,
-                 log_std_init=0.0, full_std=True, sde_net_arch=None):
+                 log_std_init=0.0, full_std=True,
+                 sde_net_arch=None, use_expln=False, squash_output=False):
         super(PPOPolicy, self).__init__(observation_space, action_space, device)
         self.obs_dim = self.observation_space.shape[0]
 
@@ -63,8 +69,8 @@ class PPOPolicy(BasePolicy):
         if use_sde:
             dist_kwargs = {
                 'full_std': full_std,
-                'squash_output': False,
-                'use_expln': False,
+                'squash_output': squash_output,
+                'use_expln': use_expln,
                 'learn_features': sde_net_arch is not None
             }