ep,
learner=learner.RandomPolicy(),
episode_count=eval_episode_count,
iteration_count=iteration_count,
epsilon=1.0, # purely random
render=False,
verbosity=Verbosity.Quiet,
title="Random search")
# %%
# Plot averaged cumulative rewards for DQL vs Random vs DQL-Exploit
themodel = dqla.CyberBattleStateActionModel(ep)
p.plot_averaged_cummulative_rewards(
all_runs=[best_dqn_learning_run_10, random_run, dql_exploit_run],
title=
f'Benchmark -- max_nodes={ep.maximum_node_count}, episodes={eval_episode_count},\n'
f'State: {[f.name() for f in themodel.state_space.feature_selection]} '
f'({len(themodel.state_space.feature_selection)}\n'
f"Action: abstract_action ({themodel.action_space.flat_size()})")
# %%
# plot cumulative rewards for all episodes
p.plot_all_episodes(best_dqn_learning_run_10)
##################################################
# %%
# %%
best_dqn_4 = learner.epsilon_greedy_search(
cyberbattle_gym_env=cyberbattlechain_4,
environment_properties=ep,
)
# %%
# Plot averaged cumulative rewards for Q-learning vs Random vs Q-Exploit
all_runs = [*qlearning_results,
randomlearning_results,
nolearning_results
]
Q_source_10 = cast(a.QTabularLearner, qlearning_bestrun_10['learner']).qsource
Q_attack_10 = cast(a.QTabularLearner, qlearning_bestrun_10['learner']).qattack
p.plot_averaged_cummulative_rewards(
all_runs=all_runs,
title=f'Benchmark -- max_nodes={ep.maximum_node_count}, episodes={eval_episode_count},\n'
f'dimension={Q_source_10.state_space.flat_size()}x{Q_source_10.action_space.flat_size()}, '
f'{Q_attack_10.state_space.flat_size()}x{Q_attack_10.action_space.flat_size()}\n'
f'Q1={[f.name() for f in Q_source_10.state_space.feature_selection]} '
f'-> {[f.name() for f in Q_source_10.action_space.feature_selection]})\n'
f"Q2={[f.name() for f in Q_attack_10.state_space.feature_selection]} -> 'action'")
# %%
# plot cumulative rewards for all episodes
p.plot_all_episodes(qlearning_results[0])
# %%
# Plot the Q-matrices
# %%
# Print non-zero coordinate in the Q matrix Q_source
title="Random+CredLookup"
)
# %%
randomlearning_results = learner.epsilon_greedy_search(
cyberbattlechain_10,
environment_properties=ep,
learner=CredentialCacheExploiter(),
episode_count=eval_episode_count,
iteration_count=iteration_count,
epsilon=1.0, # purely random
render=False,
verbosity=Verbosity.Quiet,
title="Random search"
)
# %%
p.plot_episodes_length([credexplot])
p.plot_all_episodes(credexplot)
all_runs = [credexplot,
randomlearning_results
]
p.plot_averaged_cummulative_rewards(
title=f'Benchmark -- max_nodes={ep.maximum_node_count}, episodes={eval_episode_count},\n',
all_runs=all_runs)
# %%
learner=rca.CredentialCacheExploiter(),
episode_count=10,
iteration_count=iteration_count,
epsilon=0.90,
render=False,
epsilon_exponential_decay=10000,
epsilon_minimum=0.10,
verbosity=Verbosity.Quiet,
title="Credential lookups (ϵ-greedy)")
# %%
# Plots
all_runs = [credlookup_run, dqn_with_defender, dql_exploit_run]
p.plot_averaged_cummulative_rewards(
all_runs=all_runs,
title=
f'Attacker agents vs Basic Defender -- rewards\n env={cyberbattlechain_defender.name}, episodes={training_episode_count}'
)
# p.plot_episodes_length(all_runs)
p.plot_averaged_availability(
title=
f"Attacker agents vs Basic Defender -- availability\n env={cyberbattlechain_defender.name}, episodes={training_episode_count}",
all_runs=all_runs)
# %%
# %%
# %%
# %%
# Compare and plot results for all the agents
all_runs = [
random_run,
credlookup_run,
tabularq_run,
tabularq_exploit_run,
dql_run,
dql_exploit_run
]
# Plot averaged cumulative rewards for DQL vs Random vs DQL-Exploit
themodel = dqla.CyberBattleStateActionModel(ep)
p.plot_averaged_cummulative_rewards(
all_runs=all_runs,
title=f'Benchmark -- max_nodes={ep.maximum_node_count}, episodes={eval_episode_count},\n'
f'State: {[f.name() for f in themodel.state_space.feature_selection]} '
f'({len(themodel.state_space.feature_selection)}\n'
f"Action: abstract_action ({themodel.action_space.flat_size()})")
# %%
contenders = [
credlookup_run,
tabularq_run,
dql_run,
dql_exploit_run
]
p.plot_episodes_length(contenders)
p.plot_averaged_cummulative_rewards(
title=f'Agent Benchmark top contenders\n'
f'max_nodes:{ep.maximum_node_count}\n',
all_runs=contenders)
iteration_count=iteration_count,
epsilon=0.00,
epsilon_minimum=0.00,
render=False,
verbosity=Verbosity.Quiet,
title="Exploiting DQL")
# %%
all_runs = [
random_run, credlookup_run, tabularq_run, tabularq_exploit_run, dql_run,
dql_exploit_run
]
p.plot_episodes_length(all_runs)
p.plot_averaged_cummulative_rewards(title=f'Agent Benchmark\n'
f'max_nodes:{ep.maximum_node_count}\n',
all_runs=all_runs)
# %%
contenders = [credlookup_run, tabularq_run, dql_run, dql_exploit_run]
p.plot_episodes_length(contenders)
p.plot_averaged_cummulative_rewards(title=f'Agent Benchmark top contenders\n'
f'max_nodes:{ep.maximum_node_count}\n',
all_runs=contenders)
# %%
for r in contenders:
p.plot_all_episodes(r)
# %%
