fix model loading in live mode

crypto/gogo2/main.py

@@ -1695,7 +1695,22 @@ class Agent:
         
     def load(self, path="models/trading_agent.pt"):
         if os.path.isfile(path):
-            checkpoint = torch.load(path)
+            try:
+                # First try with weights_only=True (safer)
+                checkpoint = torch.load(path, weights_only=True)
+            except Exception as e:
+                logger.warning(f"Failed to load with weights_only=True: {e}")
+                try:
+                    # Try with safe_globals for numpy.scalar
+                    import numpy as np
+                    from torch.serialization import safe_globals
+                    with safe_globals([np.core.multiarray.scalar]):
+                        checkpoint = torch.load(path, weights_only=True)
+                except Exception as e2:
+                    logger.warning(f"Failed with safe_globals: {e2}")
+                    # Fall back to weights_only=False if needed
+                    checkpoint = torch.load(path, weights_only=False)
+                
             self.policy_net.load_state_dict(checkpoint['policy_net'])
             self.target_net.load_state_dict(checkpoint['target_net'])
             self.optimizer.load_state_dict(checkpoint['optimizer'])
@@ -2154,6 +2169,13 @@ async def live_trading(agent, env, exchange, demo=True):
         # Main trading loop
         step_counter = 0
         
+        # For online learning
+        states = []
+        actions = []
+        rewards = []
+        next_states = []
+        dones = []
+        
         while True:
             # Wait for the next candle (1 minute)
             await asyncio.sleep(5)  # Check every 5 seconds
@@ -2175,7 +2197,38 @@ async def live_trading(agent, env, exchange, demo=True):
             action = agent.select_action(state, training=False)
             
             # Take action
-            _, reward, _ = env.step(action)
+            next_state, reward, done = env.step(action)
+            
+            # Store experience for online learning
+            states.append(state)
+            actions.append(action)
+            rewards.append(reward)
+            next_states.append(next_state)
+            dones.append(done)
+            
+            # Online learning - update the model with new experiences
+            if len(states) >= 10:  # Batch size for online learning
+                # Store experiences in replay memory
+                for i in range(len(states)):
+                    agent.memory.push(states[i], actions[i], rewards[i], next_states[i], dones[i])
+                
+                # Learn from experiences if we have enough samples
+                if len(agent.memory) > 32:
+                    loss = agent.learn()
+                    if loss is not None:
+                        agent.writer.add_scalar('Live/Loss', loss, step_counter)
+                
+                # Clear the temporary storage
+                states = []
+                actions = []
+                rewards = []
+                next_states = []
+                dones = []
+                
+                # Save the updated model periodically
+                if step_counter % 100 == 0:
+                    agent.save("models/trading_agent_live_updated.pt")
+                    logger.info("Updated model saved during live trading")
             
             # Log trading activity
             action_names = ["HOLD", "BUY", "SELL", "CLOSE"]