done?

hw2/model.py

@@ -0,0 +1,53 @@
+from hydra.utils import instantiate
+import torch
+import torch.nn as nn
+
+
+class QNetwork(nn.Module):
+    def __init__(self, state_size, action_size, hidden_size, activation):
+        super(QNetwork, self).__init__()
+        self.q_head = nn.Sequential(
+            nn.Linear(state_size, hidden_size),
+            instantiate(activation),
+            nn.Linear(hidden_size, hidden_size),
+            instantiate(activation),
+            nn.Linear(hidden_size, action_size)
+        )
+
+    def forward(self, state):
+        Qs = self.q_head(state)
+        return Qs
+
+
+class DuelingQNetwork(nn.Module):
+    def __init__(self, state_size, action_size, hidden_size, activation):
+        super(DuelingQNetwork, self).__init__()
+        self.feature_layer = nn.Sequential(
+            nn.Linear(state_size, hidden_size),
+            instantiate(activation),
+        )
+        self.value_head = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            instantiate(activation),
+            nn.Linear(hidden_size, 1)
+        )
+        self.advantage_head = nn.Sequential(
+            nn.Linear(hidden_size, hidden_size),
+            instantiate(activation),
+            nn.Linear(hidden_size, action_size)
+        )
+
+    def forward(self, state):
+        """
+        Get the Q value of the current state and action using dueling network
+        """
+        ############################
+        # YOUR IMPLEMENTATION HERE #
+
+        # using equation (7) on https://arxiv.org/pdf/1511.06581
+        Qs=self.value_head(self.feature_layer(state))+self.advantage_head(self.feature_layer(state))
+        ############################
+        return Qs
+
+
+