training wip

NN/models/enhanced_cnn.py

@@ -80,6 +80,9 @@ class EnhancedCNN(nn.Module):
         self.n_actions = n_actions
         self.confidence_threshold = confidence_threshold
         
+        # Training data storage
+        self.training_data = []
+        
         # Calculate input dimensions
         if isinstance(input_shape, (list, tuple)):
             if len(input_shape) == 3:  # [channels, height, width]
@@ -648,6 +651,30 @@ class EnhancedCNN(nn.Module):
                 'strength': 0.0,
                 'weighted_strength': 0.0
             }
+    
+    def add_training_data(self, state, action, reward):
+        """
+        Add training data to the model's training buffer
+        
+        Args:
+            state: Input state
+            action: Action taken
+            reward: Reward received
+        """
+        try:
+            self.training_data.append({
+                'state': state,
+                'action': action,
+                'reward': reward,
+                'timestamp': time.time()
+            })
+            
+            # Keep only the last 1000 training samples
+            if len(self.training_data) > 1000:
+                self.training_data = self.training_data[-1000:]
+                
+        except Exception as e:
+            logger.error(f"Error adding training data: {e}")
         
     def save(self, path):
         """Save model weights and architecture"""