#1386: remove redundant config files + test fixtures + fixing deterministic and seed config description in documentation to avoid misunderstandings
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Czar Echavez
@@ -310,11 +310,11 @@ Rewards are calculated based on the difference between the current state and ref
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* **deterministic** [bool]
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Set to true if the agent should use deterministic actions. Default is ``False``
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Set to true if the agent evaluation should be deterministic. Default is ``False``
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* **seed** [int]
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Seed used in the randomisation in training / evaluation. Default is ``None``
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Seed used in the randomisation in agent training. Default is ``None``
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The Lay Down Config
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*******************
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@@ -1,155 +0,0 @@
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# Training Config File
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# Sets which agent algorithm framework will be used.
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# Options are:
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# "SB3" (Stable Baselines3)
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# "RLLIB" (Ray RLlib)
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# "CUSTOM" (Custom Agent)
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agent_framework: SB3
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# Sets which deep learning framework will be used (by RLlib ONLY).
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# Default is TF (Tensorflow).
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# Options are:
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# "TF" (Tensorflow)
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# TF2 (Tensorflow 2.X)
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# TORCH (PyTorch)
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deep_learning_framework: TF2
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# Sets which Agent class will be used.
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# Options are:
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# "A2C" (Advantage Actor Critic coupled with either SB3 or RLLIB agent_framework)
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# "PPO" (Proximal Policy Optimization coupled with either SB3 or RLLIB agent_framework)
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# "HARDCODED" (The HardCoded agents coupled with an ACL or NODE action_type)
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# "DO_NOTHING" (The DoNothing agents coupled with an ACL or NODE action_type)
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# "RANDOM" (primaite.agents.simple.RandomAgent)
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# "DUMMY" (primaite.agents.simple.DummyAgent)
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agent_identifier: A2C
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# Sets whether Red Agent POL and IER is randomised.
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# Options are:
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# True
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# False
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random_red_agent: False
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# The (integer) seed to be used in random number generation
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# Default is None (null)
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seed: 12345
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# Set whether the agent will be deterministic instead of stochastic
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# Options are:
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# True
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# False
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deterministic: True
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# Sets what view of the environment the deterministic hardcoded agent has. The default is BASIC.
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# Options are:
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# "BASIC" (The current observation space only)
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# "FULL" (Full environment view with actions taken and reward feedback)
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hard_coded_agent_view: FULL
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# Sets How the Action Space is defined:
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# "NODE"
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# "ACL"
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# "ANY" node and acl actions
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action_type: NODE
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# observation space
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observation_space:
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# flatten: true
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components:
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- name: NODE_LINK_TABLE
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# - name: NODE_STATUSES
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# - name: LINK_TRAFFIC_LEVELS
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# Number of episodes to run per session
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num_episodes: 10
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# Number of time_steps per episode
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num_steps: 256
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# Sets how often the agent will save a checkpoint (every n time episodes).
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# Set to 0 if no checkpoints are required. Default is 10
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checkpoint_every_n_episodes: 0
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# Time delay (milliseconds) between steps for CUSTOM agents.
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time_delay: 5
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# Type of session to be run. Options are:
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# "TRAIN" (Trains an agent)
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# "EVAL" (Evaluates an agent)
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# "TRAIN_EVAL" (Trains then evaluates an agent)
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session_type: TRAIN_EVAL
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# Environment config values
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# The high value for the observation space
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observation_space_high_value: 1000000000
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# The Stable Baselines3 learn/eval output verbosity level:
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# Options are:
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# "NONE" (No Output)
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# "INFO" (Info Messages (such as devices and wrappers used))
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# "DEBUG" (All Messages)
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sb3_output_verbose_level: NONE
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# Reward values
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# Generic
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all_ok: 0
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# Node Hardware State
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off_should_be_on: -10
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off_should_be_resetting: -5
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on_should_be_off: -2
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on_should_be_resetting: -5
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resetting_should_be_on: -5
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resetting_should_be_off: -2
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resetting: -3
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# Node Software or Service State
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good_should_be_patching: 2
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good_should_be_compromised: 5
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good_should_be_overwhelmed: 5
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patching_should_be_good: -5
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patching_should_be_compromised: 2
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patching_should_be_overwhelmed: 2
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patching: -3
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compromised_should_be_good: -20
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compromised_should_be_patching: -20
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compromised_should_be_overwhelmed: -20
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compromised: -20
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overwhelmed_should_be_good: -20
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overwhelmed_should_be_patching: -20
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overwhelmed_should_be_compromised: -20
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overwhelmed: -20
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# Node File System State
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good_should_be_repairing: 2
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good_should_be_restoring: 2
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good_should_be_corrupt: 5
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good_should_be_destroyed: 10
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repairing_should_be_good: -5
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repairing_should_be_restoring: 2
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repairing_should_be_corrupt: 2
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repairing_should_be_destroyed: 0
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repairing: -3
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restoring_should_be_good: -10
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restoring_should_be_repairing: -2
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restoring_should_be_corrupt: 1
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restoring_should_be_destroyed: 2
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restoring: -6
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corrupt_should_be_good: -10
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corrupt_should_be_repairing: -10
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corrupt_should_be_restoring: -10
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corrupt_should_be_destroyed: 2
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corrupt: -10
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destroyed_should_be_good: -20
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destroyed_should_be_repairing: -20
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destroyed_should_be_restoring: -20
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destroyed_should_be_corrupt: -20
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destroyed: -20
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scanning: -2
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# IER status
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red_ier_running: -5
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green_ier_blocked: -10
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# Patching / Reset durations
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os_patching_duration: 5 # The time taken to patch the OS
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node_reset_duration: 5 # The time taken to reset a node (hardware)
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service_patching_duration: 5 # The time taken to patch a service
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file_system_repairing_limit: 5 # The time take to repair the file system
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file_system_restoring_limit: 5 # The time take to restore the file system
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file_system_scanning_limit: 5 # The time taken to scan the file system
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@@ -1,21 +0,0 @@
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from __future__ import annotations
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from pathlib import Path
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import polars as pl
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def compare_transaction_file(output_a_file_path: str | Path, output_b_file_path: str | Path):
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"""Function used to check if contents of transaction files are the same."""
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# load output a file
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data_a = pl.read_csv(output_a_file_path)
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# load output b file
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data_b = pl.read_csv(output_b_file_path)
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# remove the time stamp column
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data_a = data_a.drop("Timestamp")
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data_b = data_b.drop("Timestamp")
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# if the comparison is empty, both files are the same i.e. True
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return data_a.frame_equal(data_b)
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