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Merge remote-tracking branch 'origin/dev' into feature/917_Integrate_with_RLLib

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# Conflicts:
#	tests/test_reward.py
This commit is contained in:
Chris McCarthy
2023-07-03 11:59:16 +01:00
parent c38c13b829 4f0f542570
commit 123ec8343c
1 changed files with 18 additions and 10 deletions
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tests/test_reward.py
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@@ -21,19 +21,27 @@ def test_rewards_are_being_penalised_at_each_step_function(
When the initial state is OFF compared to reference state which is ON.
On different steps (of the 13 in total) these are the following rewards
for config_6 which are activated:
File System State: goodShouldBeCorrupt = 5 (between Steps 1 & 3)
Hardware State: onShouldBeOff = -2 (between Steps 4 & 6)
Service State: goodShouldBeCompromised = 5 (between Steps 7 & 9)
Software State (Software State): goodShouldBeCompromised = 5 (between
Steps 10 & 12)
The config 'one_node_states_on_off_lay_down_config.yaml' has 15 steps:
On different steps, the laydown config has Pattern of Life (PoLs) which change a state of the node's attribute.
For example, turning the nodes' file system state to CORRUPT from its original state GOOD.
As a result these are the following rewards are activated:
File System State: corrupt_should_be_good = -10 * 2 (on Steps 1 & 2)
Hardware State: off_should_be_on = -10 * 2 (on Steps 4 & 5)
Service State: compromised_should_be_good = -20 * 2 (on Steps 7 & 8)
Software State: compromised_should_be_good = -20 * 2 (on Steps 10 & 11)
Total Reward: -2 - 2 + 5 + 5 + 5 + 5 + 5 + 5 = 26
Step Count: 13
The Pattern of Life (PoLs) last for 2 steps, so the agent is penalised twice.
Note: This test run inherits from conftest.py where the PrimAITE environment is ran and the blue agent is hard-coded
to do NOTHING on every step.
We use Pattern of Lifes (PoLs) to change the nodes states and display that the agent is being penalised on all steps
where the live network node differs from the network reference node.
Total Reward: -10 + -10 + -10 + -10 + -20 + -20 + -20 + -20 = -120
Step Count: 15
For the 4 steps where this occurs the average reward is:
Average Reward: 2 (26 / 13)
Average Reward: -8 (-120 / 15)
"""
with temp_primaite_session as session:
session.evaluate()