diff --git a/sac.py b/sac.py
index 7d22153..910d2fd 100644
--- a/sac.py
+++ b/sac.py
@@ -16,7 +16,6 @@ class SAC(object):
         self.gamma = args.gamma
         self.tau = args.tau
         self.alpha = args.alpha
-        self.reparam = args.reparam
         self.policy_type = args.policy
         self.target_update_interval = args.target_update_interval
 
@@ -46,7 +45,7 @@ class SAC(object):
         state = torch.FloatTensor(state).unsqueeze(0)
         if eval == False:
             self.policy.train()
-            _, _, action, _, _ = self.policy.evaluate(state)
+            action, _, _, _, _ = self.policy.evaluate(state)
         else:
             self.policy.eval()
             _, _, _, action, _ = self.policy.evaluate(state)
@@ -73,7 +72,7 @@ class SAC(object):
         up training, especially on harder task.
         """
         expected_q1_value, expected_q2_value = self.critic(state_batch, action_batch)
-        new_action, log_prob, x_t, mean, log_std = self.policy.evaluate(state_batch, reparam=self.reparam)
+        new_action, log_prob, _, mean, log_std = self.policy.evaluate(state_batch)
 
         if self.policy_type == "Gaussian":
             """
@@ -113,21 +112,17 @@ class SAC(object):
             """
             next_value = expected_new_q_value - (self.alpha * log_prob)
             value_loss = self.value_criterion(expected_value, next_value.detach())
-            log_prob_target = expected_new_q_value - expected_value
         else:
-            log_prob_target = expected_new_q_value
+            pass
 
-        if self.reparam == True:
-            """
-            Reparameterization trick is used to get a low variance estimator
-            f(εt;st) = action sampled from the policy
-            εt is an input noise vector, sampled from some fixed distribution
-            Jπ = 𝔼st∼D,εt∼N[logπ(f(εt;st)|st)−Q(st,f(εt;st))]
-            ∇Jπ =∇log π + ([∇at log π(at|st) − ∇at Q(st,at)])∇f(εt;st)
-            """
-            policy_loss = ((self.alpha * log_prob) - expected_new_q_value).mean()
-        else:
-            policy_loss = (log_prob * (log_prob - log_prob_target).detach()).mean() # likelihood ratio gradient estimator
+        """
+        Reparameterization trick is used to get a low variance estimator
+        f(εt;st) = action sampled from the policy
+        εt is an input noise vector, sampled from some fixed distribution
+        Jπ = 𝔼st∼D,εt∼N[logπ(f(εt;st)|st)−Q(st,f(εt;st))]
+        ∇Jπ =∇log π + ([∇at log π(at|st) − ∇at Q(st,at)])∇f(εt;st)
+        """
+        policy_loss = ((self.alpha * log_prob) - expected_new_q_value).mean()
 
         # Regularization Loss
         mean_loss = 0.001 * mean.pow(2).mean()