diff --git a/cares_reinforcement_learning/util/NetworkFactory.py b/cares_reinforcement_learning/util/NetworkFactory.py
index d20f8de7..791ef868 100644
--- a/cares_reinforcement_learning/util/NetworkFactory.py
+++ b/cares_reinforcement_learning/util/NetworkFactory.py
@@ -1,7 +1,7 @@
 import torch
 import logging
-
 from cares_reinforcement_learning.util.configurations import AlgorithmConfig
+import sys, inspect
 
 def create_DQN(observation_size, action_num, config: AlgorithmConfig):
     from cares_reinforcement_learning.algorithm.value import DQN
@@ -35,7 +35,7 @@ def create_DuelingDQN(observation_size, action_num, config: AlgorithmConfig):
     return agent
 
 
-def create_DDQN(observation_size, action_num, config: AlgorithmConfig):
+def create_DoubleDQN(observation_size, action_num, config: AlgorithmConfig):
     from cares_reinforcement_learning.algorithm.value import DoubleDQN
     from cares_reinforcement_learning.networks.DoubleDQN import Network
 
@@ -170,21 +170,20 @@ def create_NaSATD3(observation_size, action_num, config: AlgorithmConfig):
 class NetworkFactory:
     def create_network(self, observation_size, action_num, config: AlgorithmConfig):
         algorithm = config.algorithm
-        if algorithm == "DQN":
-            return create_DQN(observation_size, action_num, config)
-        elif algorithm == "DoubleDQN":
-            return create_DDQN(observation_size, action_num, config)
-        elif algorithm == "DuelingDQN":
-            return create_DuelingDQN(observation_size, action_num, config)
-        elif algorithm == "PPO":
-            return create_PPO(observation_size, action_num, config)
-        elif algorithm == "DDPG":
-            return create_DDPG(observation_size, action_num, config)
-        elif algorithm == "SAC":
-            return create_SAC(observation_size, action_num, config)
-        elif algorithm == "TD3":
-            return create_TD3(observation_size, action_num, config)
-        elif algorithm == "NaSATD3":
-            return create_NaSATD3(observation_size, action_num, config)
-        logging.warn(f"Algorithm: {algorithm} is not in the default cares_rl factory")
-        return None
+
+        '''
+        Method taken from:
+        https://stackoverflow.com/questions/1796180/how-can-i-get-a-list-of-all-classes-within-current-module-in-python
+        '''
+
+        agent = None
+        
+        for name, obj in inspect.getmembers(sys.modules[__name__]):
+            if inspect.isfunction(obj):
+                if name == f"create_{algorithm}":
+                    agent = obj(observation_size, action_num, config)
+        
+        if agent is None:
+            logging.warn(f"Unkown failed to return None: returned {agent}")
+    
+        return agent
diff --git a/tests/test_utils.py b/tests/test_utils.py
index af6a9cbb..069857dd 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -13,7 +13,7 @@ def test_create_agents():
     agent = create_DuelingDQN(10, 5, DuelingDQNConfig())
     assert isinstance(agent, DQN), "Failed to create DuelingDQN agent"
 
-    agent = create_DDQN(10, 5, DoubleDQNConfig())
+    agent = create_DoubleDQN(10, 5, DoubleDQNConfig())
     assert isinstance(agent, DoubleDQN), "Failed to create DDQN agent"
 
     agent = create_PPO(10, 5,PPOConfig())