unify model names

NN/training/model_manager.py

@@ -532,22 +532,18 @@ class ModelManager:
         if not self.legacy_checkpoints_dir.exists():
             return None
 
-        # Define search patterns for different model types
-        # Handle both orchestrator naming and direct model naming
-        model_patterns = {
-            'dqn_agent': ['dqn_agent', 'dqn', 'agent'],
-            'enhanced_cnn': ['cnn_model', 'enhanced_cnn', 'cnn', 'optimized_short_term'],
-            'cob_rl': ['cob_rl', 'rl', 'rl_agent', 'trading_agent'],
-            'transformer': ['transformer', 'decision'],
-            'decision': ['decision', 'transformer'],
-            # Also support direct model names
-            'dqn': ['dqn_agent', 'dqn', 'agent'],
-            'cnn': ['cnn_model', 'cnn', 'optimized_short_term'],
-            'rl': ['cob_rl', 'rl', 'rl_agent']
-        }
+        # Use unified model naming throughout the project
+        # All model references use consistent short names: dqn, cnn, cob_rl, transformer, decision
+        # This eliminates complex mapping and ensures consistency across the entire codebase
+        patterns = [model_name]
 
-        # Get patterns for this model name or use generic patterns
-        patterns = model_patterns.get(model_name, [model_name])
+        # Add minimal backward compatibility patterns
+        if model_name == 'dqn':
+            patterns.extend(['dqn_agent', 'agent'])
+        elif model_name == 'cnn':
+            patterns.extend(['cnn_model', 'enhanced_cnn'])
+        elif model_name == 'cob_rl':
+            patterns.extend(['rl', 'rl_agent', 'trading_agent'])
 
         # Search in legacy saved directory first
         legacy_saved_dir = self.legacy_checkpoints_dir / "saved"
@@ -558,7 +554,7 @@ class ModelManager:
                     return file_path
 
         # Search in model-specific directories
-        for model_type in model_patterns.keys():
+        for model_type in ['cnn', 'dqn', 'rl', 'transformer', 'decision']:
             model_dir = self.legacy_checkpoints_dir / model_type
             if model_dir.exists():
                 saved_dir = model_dir / "saved"