Get the db admin green agent working
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from primaite.game.agent.actions import ActionManager
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from primaite.game.agent.observations import ICSObservation, ObservationManager
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from primaite.game.agent.rewards import RewardFunction
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from primaite.game.agent.scripted_agents import ProbabilisticAgent
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def test_probabilistic_agent():
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"""
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Check that the probabilistic agent selects actions with approximately the right probabilities.
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Using a binomial probability calculator (https://www.wolframalpha.com/input/?i=binomial+distribution+calculator),
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we can generate some lower and upper bounds of how many times we expect the agent to take each action. These values
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were chosen to guarantee a less than 1 in a million chance of the test failing due to unlucky random number
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generation.
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"""
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N_TRIALS = 10_000
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P_DO_NOTHING = 0.1
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P_NODE_APPLICATION_EXECUTE = 0.3
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P_NODE_FILE_DELETE = 0.6
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MIN_DO_NOTHING = 850
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MAX_DO_NOTHING = 1150
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MIN_NODE_APPLICATION_EXECUTE = 2800
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MAX_NODE_APPLICATION_EXECUTE = 3200
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MIN_NODE_FILE_DELETE = 5750
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MAX_NODE_FILE_DELETE = 6250
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action_space = ActionManager(
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actions=[
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{"type": "DONOTHING"},
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{"type": "NODE_APPLICATION_EXECUTE"},
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{"type": "NODE_FILE_DELETE"},
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],
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nodes=[
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{
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"node_name": "client_1",
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"applications": [{"application_name": "WebBrowser"}],
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"folders": [{"folder_name": "downloads", "files": [{"file_name": "cat.png"}]}],
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},
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],
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max_folders_per_node=2,
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max_files_per_folder=2,
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max_services_per_node=2,
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max_applications_per_node=2,
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max_nics_per_node=2,
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max_acl_rules=10,
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protocols=["TCP", "UDP", "ICMP"],
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ports=["HTTP", "DNS", "ARP"],
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act_map={
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0: {"action": "DONOTHING", "options": {}},
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1: {"action": "NODE_APPLICATION_EXECUTE", "options": {"node_id": 0, "application_id": 0}},
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2: {"action": "NODE_FILE_DELETE", "options": {"node_id": 0, "folder_id": 0, "file_id": 0}},
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},
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)
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observation_space = ObservationManager(ICSObservation())
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reward_function = RewardFunction()
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pa = ProbabilisticAgent(
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agent_name="test_agent",
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action_space=action_space,
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observation_space=observation_space,
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reward_function=reward_function,
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settings={
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"action_probabilities": {0: P_DO_NOTHING, 1: P_NODE_APPLICATION_EXECUTE, 2: P_NODE_FILE_DELETE},
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"random_seed": 120,
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},
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)
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do_nothing_count = 0
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node_application_execute_count = 0
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node_file_delete_count = 0
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for _ in range(N_TRIALS):
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a = pa.get_action(0, timestep=0)
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if a == ("DONOTHING", {}):
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do_nothing_count += 1
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elif a == ("NODE_APPLICATION_EXECUTE", {"node_id": 0, "application_id": 0}):
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node_application_execute_count += 1
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elif a == ("NODE_FILE_DELETE", {"node_id": 0, "folder_id": 0, "file_id": 0}):
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node_file_delete_count += 1
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else:
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raise AssertionError("Probabilistic agent produced an unexpected action.")
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assert MIN_DO_NOTHING < do_nothing_count < MAX_DO_NOTHING
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assert MIN_NODE_APPLICATION_EXECUTE < node_application_execute_count < MAX_NODE_APPLICATION_EXECUTE
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assert MIN_NODE_FILE_DELETE < node_file_delete_count < MAX_NODE_FILE_DELETE
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