diff --git a/reinforcement/deepQLearningAgents.py b/reinforcement/deepQLearningAgents.py
index 1e8331b..5172364 100644
--- a/reinforcement/deepQLearningAgents.py
+++ b/reinforcement/deepQLearningAgents.py
@@ -73,6 +73,9 @@ class PacmanDeepQAgent(PacmanQAgent):
         else:
             print("Initializing new model parameters")
     def save_model(self, filename="para.bin"):
+        if model.kProductionMode:
+            print("in production mode, not saving model")
+            return
         print(f"Saving model parameters to {filename}")
         torch.save({
             'model_state_dict': self.model.state_dict(),
diff --git a/reinforcement/model.py b/reinforcement/model.py
index e9b82ae..e3405c9 100644
--- a/reinforcement/model.py
+++ b/reinforcement/model.py
@@ -11,7 +11,7 @@ from torch import tensor, double, optim
 from torch.nn.functional import relu, mse_loss
 import torch
 
-kProductionMode=True
+kProductionMode=False
 class DeepQNetwork(Module):
     """
     A model that uses a Deep Q-value Network (DQN) to approximate Q(s,a) as part