diff --git a/encoder.py b/encoder.py
index fd4f7c9..0c4507f 100644
--- a/encoder.py
+++ b/encoder.py
@@ -109,10 +109,7 @@ class PixelEncoder(nn.Module):
         out_dim = OUT_DIM[num_layers]
         self.fc = nn.Linear(num_filters * out_dim * out_dim, self.feature_dim * 2)
         self.ln = nn.LayerNorm(self.feature_dim * 2)
-<<<<<<< HEAD
         self.combine = nn.Linear(self.feature_dim + 6, self.feature_dim)
-=======
->>>>>>> origin/tester_1
 
         self.outputs = dict()
 
@@ -157,12 +154,8 @@ class PixelEncoder(nn.Module):
 
         out = self.reparameterize(mu, logstd)
         self.outputs['tanh'] = out
-<<<<<<< HEAD
         return out, mu, logstd
-=======
-        return out
->>>>>>> origin/tester_1
-    
+
     def copy_conv_weights_from(self, source):
         """Tie convolutional layers"""
         # only tie conv layers
diff --git a/sac_ae.py b/sac_ae.py
index eae526f..36b152f 100644
--- a/sac_ae.py
+++ b/sac_ae.py
@@ -416,15 +416,14 @@ class SacAeAgent(object):
         h_dist_enc = torch.distributions.Normal(h_mu, h_logvar.exp())
         h_dist_pred = torch.distributions.Normal(mean, std)
         enc_loss = torch.distributions.kl.kl_divergence(h_dist_enc, h_dist_pred).mean() * 1e-2
-
-        """
+        
         with torch.no_grad():
             z_pos, _ , _ = self.critic_target.encoder(next_obs_list[-1])
             z_out = self.critic_target.encoder.combine(torch.concat((z_pos, action), dim=-1))
         logits = self.lb_loss.compute_logits(h, z_out)
         labels = torch.arange(logits.shape[0]).long().to(self.device)
         lb_loss = nn.CrossEntropyLoss()(logits, labels) * 1e-2
-        """
+        
         #with torch.no_grad():
         #    z_pos, _ , _ = self.critic.encoder(next_obs_list[-1])
         #ub_loss = club_loss(state_enc["sample"], mean, state_enc["logvar"], h) * 1e-1
@@ -437,7 +436,7 @@ class SacAeAgent(object):
 
         ub_loss = torch.tensor(0.0)
         #enc_loss = torch.tensor(0.0)
-        lb_loss = torch.tensor(0.0)
+        #lb_loss = torch.tensor(0.0)
         #rec_loss = torch.tensor(0.0)
         loss = rec_loss + enc_loss + lb_loss + ub_loss
         self.encoder_optimizer.zero_grad()
diff --git a/train.py b/train.py
index 22d01a7..75314b5 100644
--- a/train.py
+++ b/train.py
@@ -28,10 +28,7 @@ def parse_args():
     parser.add_argument('--frame_stack', default=3, type=int)
     parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
     parser.add_argument('--resource_files', type=str)
-<<<<<<< HEAD
     parser.add_argument('--resource_files_test', type=str)
-=======
->>>>>>> origin/tester_1
     parser.add_argument('--total_frames', default=10000, type=int)
     # replay buffer
     parser.add_argument('--replay_buffer_capacity', default=100000, type=int)