diff --git a/DPI/models.py b/DPI/models.py
index f2d1d4c..72bb7b4 100644
--- a/DPI/models.py
+++ b/DPI/models.py
@@ -258,4 +258,40 @@ class CLUBSample(nn.Module):  # Sampled version of the CLUB estimator
 
     def forward(self, x_samples, y_samples):
         mu, logvar = self.get_mu_logvar(x_samples)
-        return - self.loglikeli(x_samples, y_samples)
\ No newline at end of file
+        return - self.loglikeli(x_samples, y_samples)
+    
+
+class ProjectionHead(nn.Module):
+    def __init__(self, state_size, action_size, hidden_size):
+        super(ProjectionHead, self).__init__()
+        self.state_size = state_size
+        self.action_size = action_size
+        self.hidden_size = hidden_size
+
+        self.projection_model = nn.Sequential(
+            nn.Linear(state_size + action_size, hidden_size),
+            nn.LayerNorm(hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.LayerNorm(hidden_size),
+            )
+    
+    def forward(self, state, action):
+        x = torch.cat([state, action], dim=-1)
+        x = self.projection_model(x)
+        return x
+    
+
+class ContrastiveHead(nn.Module):
+    def __init__(self, hidden_size, temperature=1):
+        super(ContrastiveHead, self).__init__()
+        self.hidden_size = hidden_size
+        self.temperature = temperature
+        self.W = nn.Parameter(torch.rand(self.hidden_size, self.hidden_size))
+
+    def forward(self, z_a, z_pos):
+        Wz = torch.matmul(self.W, z_pos.T)  # (z_dim,B)
+        logits = torch.matmul(z_a, Wz)  # (B,B)
+        logits = logits - torch.max(logits, 1)[0][:, None]
+        logits = logits * self.temperature
+        return logits
\ No newline at end of file