diff --git a/ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py b/ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py
index dd3a9854c4..b59ef0c494 100644
--- a/ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py
+++ b/ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py
@@ -46,22 +46,12 @@ def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
         expert_batch = self._demo_buffer.sample_mini_batch(
             mini_batch.num_experiences, 1
         )
-        loss, policy_mean_estimate, expert_mean_estimate, kl_loss = self._discriminator_network.compute_loss(
+        loss, stats_dict = self._discriminator_network.compute_loss(
             mini_batch, expert_batch
         )
         self.optimizer.zero_grad()
         loss.backward()
         self.optimizer.step()
-        stats_dict = {
-            "Losses/GAIL Discriminator Loss": loss.detach().cpu().numpy(),
-            "Policy/GAIL Policy Estimate": policy_mean_estimate.detach().cpu().numpy(),
-            "Policy/GAIL Expert Estimate": expert_mean_estimate.detach().cpu().numpy(),
-        }
-        if self._discriminator_network.use_vail:
-            stats_dict["Policy/GAIL Beta"] = (
-                self._discriminator_network.beta.detach().cpu().numpy()
-            )
-            stats_dict["Losses/GAIL KL Loss"] = kl_loss.detach().cpu().numpy()
         return stats_dict
 
 
@@ -76,7 +66,7 @@ class DiscriminatorNetwork(torch.nn.Module):
     def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
         super().__init__()
         self._policy_specs = specs
-        self.use_vail = settings.use_vail
+        self._use_vail = settings.use_vail
         self._settings = settings
 
         state_encoder_settings = NetworkSettings(
@@ -108,20 +98,20 @@ def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
         estimator_input_size = settings.encoding_size
         if settings.use_vail:
             estimator_input_size = self.z_size
-            self.z_sigma = torch.nn.Parameter(
+            self._z_sigma = torch.nn.Parameter(
                 torch.ones((self.z_size), dtype=torch.float), requires_grad=True
             )
-            self.z_mu_layer = linear_layer(
+            self._z_mu_layer = linear_layer(
                 settings.encoding_size,
                 self.z_size,
                 kernel_init=Initialization.KaimingHeNormal,
                 kernel_gain=0.1,
             )
-            self.beta = torch.nn.Parameter(
+            self._beta = torch.nn.Parameter(
                 torch.tensor(self.initial_beta, dtype=torch.float), requires_grad=False
             )
 
-        self.estimator = torch.nn.Sequential(
+        self._estimator = torch.nn.Sequential(
             linear_layer(estimator_input_size, 1), torch.nn.Sigmoid()
         )
 
@@ -166,9 +156,9 @@ def compute_estimate(
         hidden = self.encoder(encoder_input)
         z_mu: Optional[torch.Tensor] = None
         if self._settings.use_vail:
-            z_mu = self.z_mu_layer(hidden)
-            hidden = torch.normal(z_mu, self.z_sigma * use_vail_noise)
-        estimate = self.estimator(hidden)
+            z_mu = self._z_mu_layer(hidden)
+            hidden = torch.normal(z_mu, self._z_sigma * use_vail_noise)
+        estimate = self._estimator(hidden)
         return estimate, z_mu
 
     def compute_loss(
@@ -177,41 +167,53 @@ def compute_loss(
         """
         Given a policy mini_batch and an expert mini_batch, computes the loss of the discriminator.
         """
+        total_loss = torch.zeros(1)
+        stats_dict: Dict[str, np.ndarray] = {}
         policy_estimate, policy_mu = self.compute_estimate(
             policy_batch, use_vail_noise=True
         )
         expert_estimate, expert_mu = self.compute_estimate(
             expert_batch, use_vail_noise=True
         )
-        loss = -(
-            torch.log(expert_estimate * (1 - self.EPSILON))
-            + torch.log(1.0 - policy_estimate * (1 - self.EPSILON))
+        stats_dict["Policy/GAIL Policy Estimate"] = (
+            policy_estimate.mean().detach().cpu().numpy()
+        )
+        stats_dict["Policy/GAIL Expert Estimate"] = (
+            expert_estimate.mean().detach().cpu().numpy()
+        )
+        discriminator_loss = -(
+            torch.log(expert_estimate + self.EPSILON)
+            + torch.log(1.0 - policy_estimate + self.EPSILON)
         ).mean()
-        kl_loss: Optional[torch.Tensor] = None
+        stats_dict["Losses/GAIL Loss"] = discriminator_loss.detach().cpu().numpy()
+        total_loss += discriminator_loss
         if self._settings.use_vail:
             # KL divergence loss (encourage latent representation to be normal)
             kl_loss = torch.mean(
                 -torch.sum(
                     1
-                    + (self.z_sigma ** 2).log()
+                    + (self._z_sigma ** 2).log()
                     - 0.5 * expert_mu ** 2
                     - 0.5 * policy_mu ** 2
-                    - (self.z_sigma ** 2),
+                    - (self._z_sigma ** 2),
                     dim=1,
                 )
             )
-            vail_loss = self.beta * (kl_loss - self.mutual_information)
+            vail_loss = self._beta * (kl_loss - self.mutual_information)
             with torch.no_grad():
-                self.beta.data = torch.max(
-                    self.beta + self.alpha * (kl_loss - self.mutual_information),
+                self._beta.data = torch.max(
+                    self._beta + self.alpha * (kl_loss - self.mutual_information),
                     torch.tensor(0.0),
                 )
-            loss += vail_loss
+            total_loss += vail_loss
+            stats_dict["Policy/GAIL Beta"] = self._beta.detach().cpu().numpy()
+            stats_dict["Losses/GAIL KL Loss"] = kl_loss.detach().cpu().numpy()
         if self.gradient_penalty_weight > 0.0:
-            loss += self.gradient_penalty_weight * self.compute_gradient_magnitude(
-                policy_batch, expert_batch
+            total_loss += (
+                self.gradient_penalty_weight
+                * self.compute_gradient_magnitude(policy_batch, expert_batch)
             )
-        return loss, torch.mean(policy_estimate), torch.mean(expert_estimate), kl_loss
+        return total_loss, stats_dict
 
     def compute_gradient_magnitude(
         self, policy_batch: AgentBuffer, expert_batch: AgentBuffer
@@ -243,9 +245,9 @@ def compute_gradient_magnitude(
         hidden = self.encoder(encoder_input)
         if self._settings.use_vail:
             use_vail_noise = True
-            z_mu = self.z_mu_layer(hidden)
-            hidden = torch.normal(z_mu, self.z_sigma * use_vail_noise)
-        hidden = self.estimator(hidden)
+            z_mu = self._z_mu_layer(hidden)
+            hidden = torch.normal(z_mu, self._z_sigma * use_vail_noise)
+        hidden = self._estimator(hidden)
         estimate = torch.mean(torch.sum(hidden, dim=1))
         gradient = torch.autograd.grad(estimate, encoder_input)[0]
         # Norm's gradient could be NaN at 0. Use our own safe_norm