feature\1522:

Create random red agent behaviour.

tests/config/random_agent_main_config.yaml

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+# Main Config File
+
+# Generic config values
+# Choose one of these (dependent on Agent being trained)
+# "STABLE_BASELINES3_PPO"
+# "STABLE_BASELINES3_A2C"
+# "GENERIC"
+agent_identifier: GENERIC
+#
+red_agent_identifier: RANDOM
+# Sets How the Action Space is defined:
+# "NODE"
+# "ACL"
+# "ANY" node and acl actions
+action_type: ANY
+# Number of episodes to run per session
+num_episodes: 1
+# Number of time_steps per episode
+num_steps: 5
+# Time delay between steps (for generic agents)
+time_delay: 1
+# Type of session to be run (TRAINING or EVALUATION)
+session_type: TRAINING
+# Determine whether to load an agent from file
+load_agent: False
+# File path and file name of agent if you're loading one in
+agent_load_file: C:\[Path]\[agent_saved_filename.zip]
+
+# Environment config values
+# The high value for the observation space
+observation_space_high_value: 1_000_000_000
+
+# Reward values
+# Generic
+all_ok: 0
+# Node Hardware State
+off_should_be_on: -10
+off_should_be_resetting: -5
+on_should_be_off: -2
+on_should_be_resetting: -5
+resetting_should_be_on: -5
+resetting_should_be_off: -2
+resetting: -3
+# Node Software or Service State
+good_should_be_patching: 2
+good_should_be_compromised: 5
+good_should_be_overwhelmed: 5
+patching_should_be_good: -5
+patching_should_be_compromised: 2
+patching_should_be_overwhelmed: 2
+patching: -3
+compromised_should_be_good: -20
+compromised_should_be_patching: -20
+compromised_should_be_overwhelmed: -20
+compromised: -20
+overwhelmed_should_be_good: -20
+overwhelmed_should_be_patching: -20
+overwhelmed_should_be_compromised: -20
+overwhelmed: -20
+# Node File System State
+good_should_be_repairing: 2
+good_should_be_restoring: 2
+good_should_be_corrupt: 5
+good_should_be_destroyed: 10
+repairing_should_be_good: -5
+repairing_should_be_restoring: 2
+repairing_should_be_corrupt: 2
+repairing_should_be_destroyed: 0
+repairing: -3
+restoring_should_be_good: -10
+restoring_should_be_repairing: -2
+restoring_should_be_corrupt: 1
+restoring_should_be_destroyed: 2
+restoring: -6
+corrupt_should_be_good: -10
+corrupt_should_be_repairing: -10
+corrupt_should_be_restoring: -10
+corrupt_should_be_destroyed: 2
+corrupt: -10
+destroyed_should_be_good: -20
+destroyed_should_be_repairing: -20
+destroyed_should_be_restoring: -20
+destroyed_should_be_corrupt: -20
+destroyed: -20
+scanning: -2
+# IER status
+red_ier_running: -5
+green_ier_blocked: -10
+
+# Patching / Reset durations
+os_patching_duration: 5            # The time taken to patch the OS
+node_reset_duration: 5             # The time taken to reset a node (hardware)
+service_patching_duration: 5       # The time taken to patch a service
+file_system_repairing_limit: 5      # The time take to repair the file system
+file_system_restoring_limit: 5      # The time take to restore the file system
+file_system_scanning_limit: 5       # The time taken to scan the file system

tests/test_red_random_agent_behaviour.py

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+from datetime import time, datetime
+
+from primaite.environment.primaite_env import Primaite
+from tests import TEST_CONFIG_ROOT
+from tests.conftest import _get_temp_session_path
+
+
+def run_generic(env, config_values):
+    """Run against a generic agent."""
+    # Reset the environment at the start of the episode
+    env.reset()
+    for episode in range(0, config_values.num_episodes):
+        for step in range(0, config_values.num_steps):
+            # 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 = 0
+
+            # Run the simulation step on the live environment
+            obs, reward, done, info = env.step(action)
+
+            # Break if done is True
+            if done:
+                break
+
+            # Introduce a delay between steps
+            time.sleep(config_values.time_delay / 1000)
+
+        # Reset the environment at the end of the episode
+        env.reset()
+
+    env.close()
+
+
+def test_random_red_agent_behaviour():
+    """
+    Test that hardware state is penalised at each step.
+
+    When the initial state is OFF compared to reference state which is ON.
+    """
+    list_of_node_instructions = []
+    for i in range(2):
+
+        """Takes a config path and returns the created instance of Primaite."""
+        session_timestamp: datetime = datetime.now()
+        session_path = _get_temp_session_path(session_timestamp)
+
+        timestamp_str = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
+        env = Primaite(
+            training_config_path=TEST_CONFIG_ROOT / "one_node_states_on_off_main_config.yaml",
+            lay_down_config_path=TEST_CONFIG_ROOT / "one_node_states_on_off_lay_down_config.yaml",
+            transaction_list=[],
+            session_path=session_path,
+            timestamp_str=timestamp_str,
+        )
+        training_config = env.training_config
+        training_config.num_steps = env.episode_steps
+
+        # TOOD: This needs t be refactored to happen outside. Should be part of
+        # a main Session class.
+        if training_config.agent_identifier == "GENERIC":
+            run_generic(env, training_config)
+        all_red_actions = env.red_node_pol
+        list_of_node_instructions.append(all_red_actions)
+
+    # assert not (list_of_node_instructions[0].__eq__(list_of_node_instructions[1]))
+    print(list_of_node_instructions[0]["1"].get_start_step())
+    print(list_of_node_instructions[0]["1"].get_end_step())
+    print(list_of_node_instructions[0]["1"].get_target_node_id())
+    print(list_of_node_instructions[1]["1"].get_start_step())
+    print(list_of_node_instructions[1]["1"].get_end_step())
+    print(list_of_node_instructions[1]["1"].get_target_node_id())
+    assert list_of_node_instructions[0].__ne__(list_of_node_instructions[1])