diff --git a/ml-agents/mlagents/trainers/torch/distributions.py b/ml-agents/mlagents/trainers/torch/distributions.py
index 3ad4274a90..56d3c984b2 100644
--- a/ml-agents/mlagents/trainers/torch/distributions.py
+++ b/ml-agents/mlagents/trainers/torch/distributions.py
@@ -112,13 +112,13 @@ def pdf(self, value):
         ).squeeze(-1)
 
     def log_prob(self, value):
-        return torch.log(self.pdf(value))
+        return torch.log(self.pdf(value) + EPSILON)
 
     def all_log_prob(self):
-        return torch.log(self.probs)
+        return torch.log(self.probs + EPSILON)
 
     def entropy(self):
-        return -torch.sum(self.probs * torch.log(self.probs), dim=-1)
+        return -torch.sum(self.probs * torch.log(self.probs + EPSILON), dim=-1)
 
 
 class GaussianDistribution(nn.Module):
@@ -187,10 +187,13 @@ def _create_policy_branches(self, hidden_size: int) -> nn.ModuleList:
         return nn.ModuleList(branches)
 
     def _mask_branch(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        raw_probs = torch.nn.functional.softmax(logits, dim=-1) * mask
-        normalized_probs = raw_probs / torch.sum(raw_probs, dim=-1).unsqueeze(-1)
-        normalized_logits = torch.log(normalized_probs + EPSILON)
-        return normalized_logits
+        # Zero out masked logits, then subtract a large value. Technique mentionend here:
+        # https://arxiv.org/abs/2006.14171. Our implementation is ONNX and Barrcuda-friendly.
+        flipped_mask = 1.0 - mask
+        adj_logits = logits * mask - 1e8 * flipped_mask
+        probs = torch.nn.functional.softmax(adj_logits, dim=-1)
+        log_probs = torch.log(probs + EPSILON)
+        return log_probs
 
     def _split_masks(self, masks: torch.Tensor) -> List[torch.Tensor]:
         split_masks = []