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PrimAITE/PRIMAITE/Main.py

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Python

# Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
"""
PRIMAITE - main (harness) module
Coding Standards: PEP 8
"""
from sys import exc_info
import time
import yaml
import os.path
import logging
from datetime import datetime
from environment.primaite import PRIMAITE
from transactions.transactions_to_file import write_transaction_to_file
from common.config_values_main import config_values_main
from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy as PPOMlp
from stable_baselines3 import A2C
from stable_baselines3.common.env_checker import check_env
from stable_baselines3.common.evaluation import evaluate_policy
################################# FUNCTIONS ######################################
def run_generic():
"""
Run against a generic agent
"""
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()
# 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 run_stable_baselines3_ppo():
"""
Run against a stable_baselines3 PPO agent
"""
if config_values.load_agent == True:
try:
agent = PPO.load(config_values.agent_load_file, env, verbose=0, n_steps=config_values.num_steps)
except:
print("ERROR: Could not load agent at location: " + config_values.agent_load_file)
logging.error("Could not load agent")
logging.error("Exception occured", exc_info=True)
else:
agent = PPO(PPOMlp, env, verbose=0, n_steps=config_values.num_steps)
if config_values.session_type == "TRAINING":
# We're in a training session
print("Starting training session...")
logging.info("Starting training session...")
for episode in range(0, config_values.num_episodes):
agent.learn(total_timesteps=1)
save_agent(agent)
else:
# Default to being in an evaluation session
print("Starting evaluation session...")
logging.info("Starting evaluation session...")
evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
env.close()
def run_stable_baselines3_a2c():
"""
Run against a stable_baselines3 A2C agent
"""
if config_values.load_agent == True:
try:
agent = A2C.load(config_values.agent_load_file, env, verbose=0, n_steps=config_values.num_steps)
except:
print("ERROR: Could not load agent at location: " + config_values.agent_load_file)
logging.error("Could not load agent")
logging.error("Exception occured", exc_info=True)
else:
agent = A2C("MlpPolicy", env, verbose=0, n_steps=config_values.num_steps)
if config_values.session_type == "TRAINING":
# We're in a training session
print("Starting training session...")
logging.info("Starting training session...")
for episode in range(0, config_values.num_episodes):
agent.learn(total_timesteps=1)
save_agent(agent)
else:
# Default to being in an evaluation session
print("Starting evaluation session...")
logging.info("Starting evaluation session...")
evaluate_policy(agent, env, n_eval_episodes=config_values.num_episodes)
env.close()
def save_agent(_agent):
"""
Persist an agent (only works for stable baselines3 agents at present)
"""
now = datetime.now() # current date and time
time = now.strftime("%Y%m%d_%H%M%S")
try:
path = 'outputs/agents/'
is_dir = os.path.isdir(path)
if not is_dir:
os.makedirs(path)
filename = "outputs/agents/agent_saved_" + time
_agent.save(filename)
logging.info("Trained agent saved as " + filename)
except Exception as e:
logging.error("Could not save agent")
logging.error("Exception occured", exc_info=True)
def configure_logging():
"""
Configures logging
"""
try:
now = datetime.now() # current date and time
time = now.strftime("%Y%m%d_%H%M%S")
filename = "logs/app_" + time + ".log"
path = 'logs/'
is_dir = os.path.isdir(path)
if not is_dir:
os.makedirs(path)
logging.basicConfig(filename=filename, filemode='w', format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
except:
print("ERROR: Could not start logging")
def load_config_values():
"""
Loads the config values from the main config file into a config object
"""
try:
# Generic
config_values.agent_identifier = config_data['agentIdentifier']
config_values.num_episodes = int(config_data['numEpisodes'])
config_values.time_delay = int(config_data['timeDelay'])
config_values.config_filename_use_case = config_data['configFilename']
config_values.session_type = config_data['sessionType']
config_values.load_agent = bool(config_data['loadAgent'])
config_values.agent_load_file = config_data['agentLoadFile']
# Environment
config_values.observation_space_high_value = int(config_data['observationSpaceHighValue'])
# Reward values
# Generic
config_values.all_ok = int(config_data['allOk'])
# Node Operating State
config_values.off_should_be_on = int(config_data['offShouldBeOn'])
config_values.off_should_be_resetting = int(config_data['offShouldBeResetting'])
config_values.on_should_be_off = int(config_data['onShouldBeOff'])
config_values.on_should_be_resetting = int(config_data['onShouldBeResetting'])
config_values.resetting_should_be_on = int(config_data['resettingShouldBeOn'])
config_values.resetting_should_be_off = int(config_data['resettingShouldBeOff'])
config_values.resetting = int(config_data['resetting'])
# Node O/S or Service State
config_values.good_should_be_patching = int(config_data['goodShouldBePatching'])
config_values.good_should_be_compromised = int(config_data['goodShouldBeCompromised'])
config_values.good_should_be_overwhelmed = int(config_data['goodShouldBeOverwhelmed'])
config_values.patching_should_be_good = int(config_data['patchingShouldBeGood'])
config_values.patching_should_be_compromised = int(config_data['patchingShouldBeCompromised'])
config_values.patching_should_be_overwhelmed = int(config_data['patchingShouldBeOverwhelmed'])
config_values.patching = int(config_data['patching'])
config_values.compromised_should_be_good = int(config_data['compromisedShouldBeGood'])
config_values.compromised_should_be_patching = int(config_data['compromisedShouldBePatching'])
config_values.compromised_should_be_overwhelmed = int(config_data['compromisedShouldBeOverwhelmed'])
config_values.compromised = int(config_data['compromised'])
config_values.overwhelmed_should_be_good = int(config_data['overwhelmedShouldBeGood'])
config_values.overwhelmed_should_be_patching = int(config_data['overwhelmedShouldBePatching'])
config_values.overwhelmed_should_be_compromised = int(config_data['overwhelmedShouldBeCompromised'])
config_values.overwhelmed = int(config_data['overwhelmed'])
# Node File System State
config_values.good_should_be_repairing = int(config_data['goodShouldBeRepairing'])
config_values.good_should_be_restoring = int(config_data['goodShouldBeRestoring'])
config_values.good_should_be_corrupt = int(config_data['goodShouldBeCorrupt'])
config_values.good_should_be_destroyed = int(config_data['goodShouldBeDestroyed'])
config_values.repairing_should_be_good = int(config_data['repairingShouldBeGood'])
config_values.repairing_should_be_restoring = int(config_data['repairingShouldBeRestoring'])
config_values.repairing_should_be_corrupt = int(config_data['repairingShouldBeCorrupt'])
config_values.repairing_should_be_destroyed = int(config_data['repairingShouldBeDestroyed'])
config_values.repairing = int(config_data['repairing'])
config_values.restoring_should_be_good = int(config_data['restoringShouldBeGood'])
config_values.restoring_should_be_repairing = int(config_data['restoringShouldBeRepairing'])
config_values.restoring_should_be_corrupt = int(config_data['restoringShouldBeCorrupt'])
config_values.restoring_should_be_destroyed = int(config_data['restoringShouldBeDestroyed'])
config_values.restoring = int(config_data['restoring'])
config_values.corrupt_should_be_good = int(config_data['corruptShouldBeGood'])
config_values.corrupt_should_be_repairing = int(config_data['corruptShouldBeRepairing'])
config_values.corrupt_should_be_restoring = int(config_data['corruptShouldBeRestoring'])
config_values.corrupt_should_be_destroyed = int(config_data['corruptShouldBeDestroyed'])
config_values.corrupt = int(config_data['corrupt'])
config_values.destroyed_should_be_good = int(config_data['destroyedShouldBeGood'])
config_values.destroyed_should_be_repairing = int(config_data['destroyedShouldBeRepairing'])
config_values.destroyed_should_be_restoring = int(config_data['destroyedShouldBeRestoring'])
config_values.destroyed_should_be_corrupt = int(config_data['destroyedShouldBeCorrupt'])
config_values.destroyed = int(config_data['destroyed'])
config_values.scanning = int(config_data['scanning'])
# IER status
config_values.red_ier_running = int(config_data['redIerRunning'])
config_values.green_ier_blocked = int(config_data['greenIerBlocked'])
# Patching / Reset durations
config_values.os_patching_duration = int(config_data['osPatchingDuration'])
config_values.node_reset_duration = int(config_data['nodeResetDuration'])
config_values.service_patching_duration = int(config_data['servicePatchingDuration'])
config_values.file_system_repairing_limit = int(config_data['fileSystemRepairingLimit'])
config_values.file_system_restoring_limit = int(config_data['fileSystemRestoringLimit'])
config_values.file_system_scanning_limit = int(config_data['fileSystemScanningLimit'])
logging.info("Training agent: " + config_values.agent_identifier)
logging.info("Training environment config: " + config_values.config_filename_use_case)
logging.info("Training cycle has " + str(config_values.num_episodes) + " episodes")
except Exception as e:
logging.error("Could not save load config data")
logging.error("Exception occured", exc_info=True)
################################# MAIN PROCESS ############################################
# Starting point
# Welcome message
print("Welcome to the Primary-level AI Training Environment (PrimAITE)")
# Configure logging
configure_logging()
# Open the main config file
try:
config_file_main = open("config/config_main.yaml", "r")
config_data = yaml.safe_load(config_file_main)
# Create a config class
config_values = config_values_main()
# Load in config data
load_config_values()
except Exception as e:
logging.error("Could not load main config")
logging.error("Exception occured", exc_info=True)
# Create a list of transactions
# A transaction is an object holding the:
# - episode #
# - step #
# - initial observation space
# - action
# - reward
# - new observation space
transaction_list = []
# Create the PRIMAITE environment
try:
env = PRIMAITE(config_values, transaction_list)
logging.info("PrimAITE environment created")
except Exception as e:
logging.error("Could not create PrimAITE environment")
logging.error("Exception occured", exc_info=True)
# Get the number of steps (which is stored in the child config file)
config_values.num_steps = env.episode_steps
# Run environment against an agent
if config_values.agent_identifier == "GENERIC":
run_generic()
elif config_values.agent_identifier == "STABLE_BASELINES3_PPO":
run_stable_baselines3_ppo()
elif config_values.agent_identifier == "STABLE_BASELINES3_A2C":
run_stable_baselines3_a2c()
print("Session finished")
logging.info("Session finished")
print("Saving transaction logs...")
logging.info("Saving transaction logs...")
write_transaction_to_file(transaction_list)
config_file_main.close
print("Finished")
logging.info("Finished")