901 - fixed test_observation_space.py, added test fixture for test_seeding_and_deterministic_session.py and increased default max number of acls
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@@ -4,9 +4,41 @@ import numpy as np
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import pytest
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from primaite.environment.observations import NodeLinkTable, NodeStatuses, ObservationsHandler
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from primaite.environment.primaite_env import Primaite
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from tests import TEST_CONFIG_ROOT
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def run_generic_set_actions(env: Primaite):
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"""Run against a generic agent with specified blue agent actions."""
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# Reset the environment at the start of the episode
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# env.reset()
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training_config = env.training_config
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for episode in range(0, training_config.num_train_episodes):
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for step in range(0, training_config.num_train_steps):
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# Send the observation space to the agent to get an action
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# TEMP - random action for now
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# action = env.blue_agent_action(obs)
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action = 0
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print("Episode:", episode, "\nStep:", step)
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if step == 2:
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# [1, 1, 2, 1, 1, 1, 1(position)]
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# NEED [1, 1, 1, 2, 1, 1, 1]
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# Creates an ACL rule
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# Allows traffic from server_1 to node_1 on port FTP
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action = 43
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elif step == 4:
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action = 96
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# Run the simulation step on the live environment
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obs, reward, done, info = env.step(action)
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# Break if done is True
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if done:
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break
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return env
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@pytest.mark.parametrize(
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"temp_primaite_session",
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[
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@@ -289,16 +321,23 @@ class TestAccessControlList:
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# Used to use env from test fixture but AtrributeError function object has no 'training_config'
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with temp_primaite_session as session:
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env = session.env
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session.learn()
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env = run_generic_set_actions(env)
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obs = env.env_obs
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"""
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Observation space at the end of the episode.
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At the start of the episode, there is a single implicit Deny rule = 1,1,1,1,1,0
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(0 represents its initial position at top of ACL list)
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(1, 1, 1, 2, 1, 2, 0) - ACTION
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On Step 5, there is a rule added at POSITION 2: 2,2,3,2,3,0
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(1, 3, 1, 2, 2, 1) - SECOND ACTION
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On Step 7, there is a second rule added at POSITION 1: 2,4,2,3,3,1
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THINK THE RULES SHOULD BE THE OTHER WAY AROUND IN THE CURRENT OBSERVATION
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"""
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print("what i am testing", obs)
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# acl rule 1
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# source is 1 should be 4
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# dest is 3 should be 2
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# [2 2 3 2 3 0 2 1?4 3?2 3 3 1 1 1 1 1 1 2]
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# np.array_equal(obs, [2, 2, 3, 2, 3, 0, 2, 4, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2])
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assert np.array_equal(obs, [2, 2, 3, 2, 3, 0, 2, 4, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2])
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# assert obs == [2, 2, 3, 2, 3, 0, 2, 4, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2]
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