901 - changed acl current obs from list to numpy.array, changed default ACL list in training_config.py to FALSE, and tried to make test_seeding_and_deterministic_session.py test without fixed reward results

src/primaite/config/training_config.py

@@ -94,13 +94,13 @@ class TrainingConfig:
     "Stable Baselines3 learn/eval output verbosity level"
 
     # Access Control List/Rules
-    apply_implicit_rule: str = True
+    apply_implicit_rule: str = False
     "User choice to have Implicit ALLOW or DENY."
 
     implicit_acl_rule: RulePermissionType = RulePermissionType.DENY
     "ALLOW or DENY implicit firewall rule to go at the end of list of ACL list."
 
-    max_number_acl_rules: int = 0
+    max_number_acl_rules: int = 10
     "Sets a limit for number of acl rules allowed in the list and environment."
 
     # Reward values

src/primaite/environment/observations.py

@@ -519,30 +519,26 @@ class AccessControlList(AbstractObservationComponent):
                         port_int = self.env.ports_list.index(port) + 2
                     else:
                         _LOGGER.info(f"Port {port} could not be found.")
+                        port_int = None
 
                 # Either do the multiply on the obs space
                 # Change the obs to
-                items_to_add = [
+                obs.extend(
-                    permission_int,
+                    [
-                    source_ip_int,
+                        permission_int,
-                    dest_ip_int,
+                        source_ip_int,
-                    protocol_int,
+                        dest_ip_int,
-                    port_int,
+                        protocol_int,
-                    position,
+                        port_int,
-                ]
+                        position,
-                position = position * 6
+                    ]
-                for item in items_to_add:
+                )
-                    # print("position", position, "\nitem", int(item))
+
-                    obs.insert(position, int(item))
-                    position += 1
             else:
-                starting_position = index * 6
+                obs.extend([0, 0, 0, 0, 0, 0])
-                for placeholder in range(6):
-                    obs.insert(starting_position, 0)
-                    starting_position += 1
 
         # print("current obs", obs, "\n" ,len(obs))
-        self.current_observation = obs
+        self.current_observation[:] = obs
 
     def generate_structure(self):
         """Return a list of labels for the components of the flattened observation space."""

tests/config/obs_tests/main_config_ACCESS_CONTROL_LIST.yaml

@@ -5,7 +5,8 @@
 # "STABLE_BASELINES3_PPO"
 # "STABLE_BASELINES3_A2C"
 # "GENERIC"
-agent_identifier: STABLE_BASELINES3_A2C
+agent_framework: SB3
+agent_identifier: PPO
 # Sets How the Action Space is defined:
 # "NODE"
 # "ACL"
@@ -21,7 +22,7 @@ apply_implicit_rule: True
 # Implicit ACL firewall rule at end of lists to be default action or no rule can be selected (ALLOW or DENY)
 implicit_acl_rule: DENY
 # Total number of ACL rules allowed in the environment
-max_number_acl_rules: 10
+max_number_acl_rules: 3
 
 observation_space:
   components:
@@ -31,7 +32,7 @@ observation_space:
 time_delay: 1
 
 # Type of session to be run (TRAINING or EVALUATION)
-session_type: TRAINING
+session_type: TRAIN
 # Determine whether to load an agent from file
 load_agent: False
 # File path and file name of agent if you're loading one in

tests/config/single_action_space_main_config.yaml

@@ -39,6 +39,8 @@ agent_load_file: C:\[Path]\[agent_saved_filename.zip]
 # The high value for the observation space
 observation_space_high_value: 1000000000
 
+# Choice whether to have an ALLOW or DENY implicit rule or not (TRUE or FALSE)
+apply_implicit_rule: True
 implicit_acl_rule: DENY
 max_number_acl_rules: 10
 # Reward values

tests/conftest.py

@@ -58,6 +58,7 @@ class TempPrimaiteSession(PrimaiteSession):
 
     def __exit__(self, type, value, tb):
         shutil.rmtree(self.session_path)
+        # shutil.rmtree(self.session_path.parent)
         _LOGGER.debug(f"Deleted temp session directory: {self.session_path}")
 
 

tests/test_observation_space.py

@@ -257,7 +257,7 @@ class TestLinkTrafficLevels:
     "temp_primaite_session",
     [
         [
-            TEST_CONFIG_ROOT / "single_action_space_fixed_blue_actions_main_config.yaml",
+            TEST_CONFIG_ROOT / "obs_tests/main_config_ACCESS_CONTROL_LIST.yaml",
             TEST_CONFIG_ROOT / "obs_tests/laydown_ACL.yaml",
         ]
     ],
@@ -273,7 +273,7 @@ class TestAccessControlList:
             env.update_environent_obs()
 
         # we have two ACLs
-        assert env.env_obs.shape == (6 * 3)
+        assert env.env_obs.shape == (18,)
 
     def test_values(self, temp_primaite_session):
         """Test that traffic values are encoded correctly.
@@ -296,7 +296,7 @@ class TestAccessControlList:
         # therefore the first and third elements should be 6 and all others 0
         # (`7` corresponds to 100% utiilsation and `6` corresponds to 87.5%-100%)
         print(obs)
-        assert np.array_equal(obs, [])
+        assert np.array_equal(obs, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2])
 
     def test_observation_space_with_implicit_rule(self, temp_primaite_session):
         """Test observation space is what is expected when an agent adds ACLs during an episode."""

tests/test_seeding_and_deterministic_session.py

@@ -11,6 +11,7 @@ from tests import TEST_CONFIG_ROOT
 )
 def test_seeded_learning(temp_primaite_session):
     """Test running seeded learning produces the same output when ran twice."""
+    """
     expected_mean_reward_per_episode = {
         1: -90.703125,
         2: -91.15234375,
@@ -23,14 +24,22 @@ def test_seeded_learning(temp_primaite_session):
         9: -112.79296875,
         10: -100.01953125,
     }
+    """
     with temp_primaite_session as session:
         assert session._training_config.seed == 67890, (
             "Expected output is based upon a agent that was trained with " "seed 67890"
         )
         session.learn()
-        actual_mean_reward_per_episode = session.learn_av_reward_per_episode()
+        actual_mean_reward_per_episode_run_1 = session.learn_av_reward_per_episode()
 
-    assert actual_mean_reward_per_episode == expected_mean_reward_per_episode
+    with temp_primaite_session as session:
+        assert session._training_config.seed == 67890, (
+            "Expected output is based upon a agent that was trained with " "seed 67890"
+        )
+        session.learn()
+        actual_mean_reward_per_episode_run_2 = session.learn_av_reward_per_episode()
+
+    assert actual_mean_reward_per_episode_run_1 == actual_mean_reward_per_episode_run_2
 
 
 @pytest.mark.skip(reason="Inconsistent results. Needs someone with RL " "knowledge to investigate further.")