diff --git a/src/primaite/environment/primaite_env.py b/src/primaite/environment/primaite_env.py
index be16590f..33af7d89 100644
--- a/src/primaite/environment/primaite_env.py
+++ b/src/primaite/environment/primaite_env.py
@@ -236,7 +236,6 @@ class Primaite(Env):
             # [0, num services] - resolves to service ID (0 = nothing, resolves to service) # noqa
             self.action_dict = self.create_node_action_dict()
             self.action_space = spaces.Discrete(len(self.action_dict))
-            print(self.action_space, "NODE action space")
         elif self.action_type == ActionType.ACL:
             _LOGGER.info("Action space type ACL selected")
             # Terms (for ACL action space):
@@ -248,12 +247,10 @@ class Primaite(Env):
             # [0, num ports] - Port (0 = any, then 1 -> x resolving to port)
             self.action_dict = self.create_acl_action_dict()
             self.action_space = spaces.Discrete(len(self.action_dict))
-            print(self.action_space, "ACL action space")
         elif self.action_type == ActionType.ANY:
             _LOGGER.info("Action space type ANY selected - Node + ACL")
             self.action_dict = self.create_node_and_acl_action_dict()
             self.action_space = spaces.Discrete(len(self.action_dict))
-            print(self.action_space, "ANY action space")
         else:
             _LOGGER.info("Invalid action type selected")
         # Set up a csv to store the results of the training
@@ -455,7 +452,6 @@ class Primaite(Env):
         Args:
             _action: The action space from the agent
         """
-        # print("intepret action")
         # At the moment, actions are only affecting nodes
         if self.action_type == ActionType.NODE:
             self.apply_actions_to_nodes(_action)
@@ -470,7 +466,6 @@ class Primaite(Env):
         ):  # Node actions in multdiscrete (array) from have len 4
             self.apply_actions_to_nodes(_action)
         else:
-            print("invalid action type found")
             logging.error("Invalid action type found")
 
     def apply_actions_to_nodes(self, _action):
@@ -1091,7 +1086,6 @@ class Primaite(Env):
             item: A config data item representing action info
         """
         self.action_type = ActionType[action_info["type"]]
-        print("action type selected: ", self.action_type)
 
     def get_steps_info(self, steps_info):
         """
@@ -1196,7 +1190,6 @@ class Primaite(Env):
         """
         # reserve 0 action to be a nothing action
         actions = {0: [1, 0, 0, 0]}
-        # print("node dict function call", self.num_nodes + 1)
         action_key = 1
         for node in range(1, self.num_nodes + 1):
             # 4 node properties (NONE, OPERATING, OS, SERVICE)
@@ -1204,14 +1197,11 @@ class Primaite(Env):
                 # Node Actions either:
                 # (NONE, ON, OFF, RESET) - operating state OR (NONE, PATCH) - OS/service state
                 # Use MAX to ensure we get them all
-                # print(self.num_services, "num services")
                 for node_action in range(4):
                     for service_state in range(self.num_services):
                         action = [node, node_property, node_action, service_state]
-                        # check to see if its a nothing aciton (has no effect)
-                        # print("action node",action)
+                        # check to see if it's a nothing action (has no effect)
                         if is_valid_node_action(action):
-                            print("true")
                             actions[action_key] = action
                             action_key += 1
 
@@ -1223,7 +1213,6 @@ class Primaite(Env):
         actions = {0: [0, 0, 0, 0, 0, 0]}
 
         action_key = 1
-        # print("node count",self.num_nodes + 1)
         # 3 possible action decisions, 0=NOTHING, 1=CREATE, 2=DELETE
         for action_decision in range(3):
             # 2 possible action permissions 0 = DENY, 1 = CREATE
@@ -1241,13 +1230,10 @@ class Primaite(Env):
                                     protocol,
                                     port,
                                 ]
-                                # print("action acl", action)
                                 # Check to see if its an action we want to include as possible i.e. not a nothing action
                                 if is_valid_acl_action_extra(action):
-                                    print("true")
                                     actions[action_key] = action
                                     action_key += 1
-                                # print("false")
 
         return actions
 
@@ -1261,8 +1247,6 @@ class Primaite(Env):
         node_action_dict = self.create_node_action_dict()
         acl_action_dict = self.create_acl_action_dict()
 
-        print(len(node_action_dict), len(acl_action_dict))
-
         # Change node keys to not overlap with acl keys
         # Only 1 nothing action (key 0) is required, remove the other
         new_node_action_dict = {
@@ -1273,6 +1257,4 @@ class Primaite(Env):
 
         # Combine the Node dict and ACL dict
         combined_action_dict = {**acl_action_dict, **new_node_action_dict}
-        print("combined_action_dict entry", combined_action_dict.items())
-        # print(len(list(combined_action_dict.values())))
         return combined_action_dict
diff --git a/tests/conftest.py b/tests/conftest.py
index dd732e78..3a99bcf6 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -184,8 +184,8 @@ def run_generic(env, config_values):
             # Send the observation space to the agent to get an action
             # TEMP - random action for now
             # action = env.blue_agent_action(obs)
-            action = env.action_space.sample()
-
+            # action = env.action_space.sample()
+            action = 0
             # Run the simulation step on the live environment
             obs, reward, done, info = env.step(action)
 
@@ -222,7 +222,6 @@ def run_generic_set_actions(env, config_values):
                 # Sets Node 1 Hardware State to OFF
                 # Does not resolve any service
                 action = 16
-            print(action, "ran")
             # Run the simulation step on the live environment
             obs, reward, done, info = env.step(action)