diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 5554ded..fafe6f6 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -22,6 +22,7 @@ New Features:
 - Added ``RecurrentPPO`` (aka PPO LSTM)
 
 Bug Fixes:
+- Fixed a bug in ``RecurrentPPO`` when calculating the masked loss functions (@rnederstigt)
 ^^^^^^^^^^
 
 Deprecations:
@@ -271,4 +272,4 @@ Stable-Baselines3 is currently maintained by `Antonin Raffin`_ (aka `@araffin`_)
 Contributors:
 -------------
 
-@ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar
+@ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt
diff --git a/sb3_contrib/ppo_recurrent/ppo_recurrent.py b/sb3_contrib/ppo_recurrent/ppo_recurrent.py
index f0920f9..3f9b6e4 100644
--- a/sb3_contrib/ppo_recurrent/ppo_recurrent.py
+++ b/sb3_contrib/ppo_recurrent/ppo_recurrent.py
@@ -378,6 +378,9 @@ class RecurrentPPO(OnPolicyAlgorithm):
                     # Convert discrete action from float to long
                     actions = rollout_data.actions.long().flatten()
 
+                # Convert mask from float to bool
+                mask = rollout_data.mask > 1e-8
+
                 # Re-sample the noise matrix because the log_std has changed
                 if self.use_sde:
                     self.policy.reset_noise(self.batch_size)
@@ -393,7 +396,7 @@ class RecurrentPPO(OnPolicyAlgorithm):
                 # Normalize advantage
                 advantages = rollout_data.advantages
                 if self.normalize_advantage:
-                    advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+                    advantages = (advantages - advantages[mask].mean()) / (advantages[mask].std() + 1e-8)
 
                 # ratio between old and new policy, should be one at the first iteration
                 ratio = th.exp(log_prob - rollout_data.old_log_prob)
@@ -401,14 +404,11 @@ class RecurrentPPO(OnPolicyAlgorithm):
                 # clipped surrogate loss
                 policy_loss_1 = advantages * ratio
                 policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
-                # Mask padded sequences
-                policy_loss_1 = policy_loss_1 * rollout_data.mask
-                policy_loss_2 = policy_loss_2 * rollout_data.mask
-                policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()
+                policy_loss = -th.mean(th.min(policy_loss_1, policy_loss_2)[mask])
 
                 # Logging
                 pg_losses.append(policy_loss.item())
-                clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()).item()
+                clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()[mask]).item()
                 clip_fractions.append(clip_fraction)
 
                 if self.clip_range_vf is None:
@@ -422,16 +422,16 @@ class RecurrentPPO(OnPolicyAlgorithm):
                     )
                 # Value loss using the TD(gae_lambda) target
                 # Mask padded sequences
-                value_loss = th.mean(((rollout_data.returns - values_pred) * rollout_data.mask) ** 2)
+                value_loss = th.mean(((rollout_data.returns - values_pred) ** 2)[mask])
 
                 value_losses.append(value_loss.item())
 
                 # Entropy loss favor exploration
                 if entropy is None:
                     # Approximate entropy when no analytical form
-                    entropy_loss = -th.mean(-(log_prob * rollout_data.mask))
+                    entropy_loss = -th.mean(-log_prob[mask])
                 else:
-                    entropy_loss = -th.mean(entropy * rollout_data.mask)
+                    entropy_loss = -th.mean(entropy[mask])
 
                 entropy_losses.append(entropy_loss.item())
 
@@ -443,7 +443,7 @@ class RecurrentPPO(OnPolicyAlgorithm):
                 # and Schulman blog: http://joschu.net/blog/kl-approx.html
                 with th.no_grad():
                     log_ratio = log_prob - rollout_data.old_log_prob
-                    approx_kl_div = th.mean((th.exp(log_ratio) - 1) - log_ratio).cpu().numpy()
+                    approx_kl_div = th.mean(((th.exp(log_ratio) - 1) - log_ratio)[mask]).cpu().numpy()
                     approx_kl_divs.append(approx_kl_div)
 
                 if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl: