Create connect4_agent.py

connect4_agent.py

@@ -0,0 +1,28 @@
+import torch
+import numpy as np
+
+class DQN(torch.nn.Module):
+    def __init__(self, input_size=42, hidden_size=128, output_size=7):
+        super(DQN, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.relu = torch.nn.ReLU()
+        self.fc2 = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        return self.fc2(x)
+
+def load_model(path):
+    model = DQN()
+    model.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
+    model.eval()
+    return model
+
+def get_best_action(board, model):
+    flat_state = torch.tensor(board.flatten(), dtype=torch.float32).unsqueeze(0)
+    with torch.no_grad():
+        q_values = model(flat_state)
+    valid_actions = [c for c in range(7) if board[0][c] == 0]
+    q_values[0, [i for i in range(7) if i not in valid_actions]] = -float('inf')
+    return torch.argmax(q_values).item()