diff --git a/torch_training/dueling_double_dqn.py b/torch_training/dueling_double_dqn.py
index dd67b4f0d73ffe1b3f4ad3e947debf18508e78b0..cf2f7d512b99aafb9fe0477bf048441efa0bff9e 100644
--- a/torch_training/dueling_double_dqn.py
+++ b/torch_training/dueling_double_dqn.py
@@ -20,7 +20,7 @@ double_dqn = True  # If using double dqn algorithm
 input_channels = 5  # Number of Input channels
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-device = torch.device("cpu")
+#device = torch.device("cpu")
 print(device)
 
 
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index ad42e0a0ec60ba56d270cfea950da32b982527d1..222430dd0af0f239ddd99d127b21349a13a2e892 100644
--- a/torch_training/multi_agent_training.py
+++ b/torch_training/multi_agent_training.py
@@ -4,6 +4,11 @@ import random
 import sys
 from collections import deque
 
+# make sure the root path is in system path
+from pathlib import Path
+base_dir = Path(__file__).resolve().parent.parent
+sys.path.append(str(base_dir))
+
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index c97f1f5df2e171410f05f482a594d2b840c42dbc..f69929f65accc53101ba28d8904cdf76b7e1cfca 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -3,6 +3,11 @@ import random
 import sys
 from collections import deque
 
+# make sure the root path is in system path
+from pathlib import Path
+base_dir = Path(__file__).resolve().parent.parent
+sys.path.append(str(base_dir))
+
 import matplotlib.pyplot as plt
 import numpy as np
 import torch