def test_sim_random_vs_random(version) -> ClientConfig:
simulation_config = SimulationConfig(log_frequency=1, num_episodes=10)
env_name = "idsgame-v" + str(version)
client_config = ClientConfig(env_name=env_name,
attacker_type=AgentType.RANDOM.value,
defender_type=AgentType.RANDOM.value,
mode=RunnerMode.SIMULATE.value,
simulation_config=simulation_config,
output_dir=default_output_dir(),
title="RandomAttacker vs RandomDefender")
Runner.run(client_config)
def test_sim_random_vs_defend_minimal(version) -> ClientConfig:
simulation_config = SimulationConfig(log_frequency=1, num_episodes=10)
env_name = "idsgame-v" + str(version)
client_config = ClientConfig(
env_name=env_name,
attacker_type=AgentType.RANDOM.value,
defender_type=AgentType.DEFEND_MINIMAL_VALUE.value,
mode=RunnerMode.SIMULATE.value,
simulation_config=simulation_config,
output_dir=default_output_dir())
Runner.run(client_config)
def test_train_random_attack_tabular_q_learning(version) -> ClientConfig:
q_agent_config = QAgentConfig(num_episodes=10,
eval_frequency=100,
attacker=False,
defender=True)
env_name = "idsgame-random_attack-v" + str(version)
client_config = ClientConfig(env_name=env_name,
defender_type=AgentType.TABULAR_Q_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER.value,
q_agent_config=q_agent_config,
output_dir=default_output_dir())
Runner.run(client_config)
def run_experiment(configpath: str, random_seed: int, noconfig: bool):
"""
Runs one experiment and saves results and plots
:param configpath: path to configfile
:param noconfig: whether to override config
:return: (train_csv_path, eval_csv_path)
"""
if configpath is not None and not noconfig:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
time_str = str(time.time())
util.create_artefact_dirs(config.output_dir, random_seed)
logger = util.setup_logger("actor_critic_vs_random_defense-v8",
config.output_dir + "/results/logs/" +
str(random_seed) + "/",
time_str=time_str)
config.pg_agent_config.save_dir = default_output_dir(
) + "/results/data/" + str(random_seed) + "/"
config.pg_agent_config.video_dir = default_output_dir(
) + "/results/videos/" + str(random_seed) + "/"
config.pg_agent_config.gif_dir = default_output_dir(
) + "/results/gifs/" + str(random_seed) + "/"
config.pg_agent_config.tensorboard_dir = default_output_dir() + "/results/tensorboard/" \
+ str(random_seed) + "/"
config.logger = logger
config.pg_agent_config.logger = logger
config.pg_agent_config.random_seed = random_seed
config.random_seed = random_seed
config.pg_agent_config.to_csv(config.output_dir +
"/results/hyperparameters/" +
str(random_seed) + "/" + time_str + ".csv")
train_csv_path = ""
eval_csv_path = ""
if config.hp_tuning:
hp_tuning.hype_grid(config)
else:
train_result, eval_result = Runner.run(config)
if len(train_result.avg_episode_steps) > 0 and len(
eval_result.avg_episode_steps) > 0:
train_csv_path = config.output_dir + "/results/data/" + str(
random_seed) + "/" + time_str + "_train" + ".csv"
train_result.to_csv(train_csv_path)
eval_csv_path = config.output_dir + "/results/data/" + str(
random_seed) + "/" + time_str + "_eval" + ".csv"
eval_result.to_csv(eval_csv_path)
plot_csv(config, eval_csv_path, train_csv_path, random_seed)
return train_csv_path, eval_csv_path
def run_experiment(configpath: str, random_seed: int, noconfig: bool):
"""
Runs one experiment and saves results and plots
:param config: experiment configuration
:param noconfig: whether to override config
:return: (train_csv_path, eval_csv_path)
"""
if configpath is not None and not noconfig:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
time_str = str(time.time())
util.create_artefact_dirs(config.output_dir, random_seed)
logger = util.setup_logger("random_attack_vs_tabular_q_learning-v3",
config.output_dir + "/results/logs/" +
str(random_seed) + "/",
time_str=time_str)
config.q_agent_config.save_dir = default_output_dir(
) + "/results/data/" + str(random_seed) + "/"
config.q_agent_config.video_dir = default_output_dir(
) + "/results/videos/" + str(random_seed) + "/"
config.q_agent_config.gif_dir = default_output_dir(
) + "/results/gifs/" + str(random_seed) + "/"
config.logger = logger
config.q_agent_config.logger = logger
config.q_agent_config.random_seed = random_seed
config.random_seed = random_seed
config.q_agent_config.to_csv(config.output_dir +
"/results/hyperparameters/" +
str(random_seed) + "/" + time_str + ".csv")
train_result, eval_result = Runner.run(config)
train_csv_path = ""
eval_csv_path = ""
if len(train_result.avg_episode_steps) > 0 and len(
eval_result.avg_episode_steps) > 0:
train_csv_path = config.output_dir + "/results/data/" + str(
random_seed) + "/" + time_str + "_train" + ".csv"
train_result.to_csv(train_csv_path)
eval_csv_path = config.output_dir + "/results/data/" + str(
random_seed) + "/" + time_str + "_eval" + ".csv"
eval_result.to_csv(eval_csv_path)
plot_csv(config, eval_csv_path, train_csv_path, random_seed)
return train_csv_path, eval_csv_path
attacker_cumulative_reward=df["attacker_cumulative_reward"],
defender_cumulative_reward=df["defender_cumulative_reward"],
log_frequency=config.simulation_config.log_frequency,
output_dir=config.output_dir, eval=False, sim=True)
# Program entrypoint
if __name__ == '__main__':
args = util.parse_args(default_config_path())
if args.configpath is not None:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
time_str = str(time.time())
util.create_artefact_dirs(config.output_dir)
logger = util.setup_logger("idsgame-v0-random_vs_defend_minimal", config.output_dir + "/logs/",
time_str=time_str)
config.logger = logger
config.simulation_config.logger = logger
config.simulation_config.to_csv(config.output_dir + "/hyperparameters/" + time_str + ".csv")
result = Runner.run(config)
if len(result.avg_episode_steps) > 0:
csv_path = config.output_dir + "/data/" + time_str + "_simulation" + ".csv"
result.to_csv(csv_path)
plot_csv(config, csv_path)
return client_config
def write_default_config(path:str = None) -> None:
"""
Writes the default configuration to a json file
:param path: the path to write the configuration to
:return: None
"""
if path is None:
path = default_config_path()
config = default_config()
util.write_config_file(config, path)
# Program entrypoint
if __name__ == '__main__':
args = util.parse_args(default_config_path())
if args.configpath is not None:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
util.create_artefact_dirs(config.output_dir, 0)
Runner.run(config)
config.q_agent_config.eval_frequency, config.q_agent_config.eval_log_frequency,
config.q_agent_config.eval_episodes, config.output_dir, sim=False)
# Program entrypoint
if __name__ == '__main__':
args = util.parse_args(default_config_path())
if args.configpath is not None:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
time_str = str(time.time())
util.create_artefact_dirs(config.output_dir)
logger = util.setup_logger("tabular_q_learning_vs_minimal_defense-v4", config.output_dir + "/logs/",
time_str=time_str)
config.logger = logger
config.q_agent_config.logger = logger
config.q_agent_config.to_csv(config.output_dir + "/hyperparameters/" + time_str + ".csv")
train_result, eval_result = Runner.run(config)
if len(train_result.avg_episode_steps) > 0 and len(eval_result.avg_episode_steps) > 0:
train_csv_path = config.output_dir + "/data/" + time_str + "_train" + ".csv"
train_result.to_csv(train_csv_path)
eval_csv_path = config.output_dir + "/data/" + time_str + "_eval" + ".csv"
eval_result.to_csv(eval_csv_path)
plot_csv(config, eval_csv_path, train_csv_path)
def hype_grid(client_config: ClientConfig):
"""
Grid search for hyperparameter tuning
:param client_config: client config for the experiment
:param param_1: the name of the first hyperparameter
:param param_2: the name of the second hyperparameter
:return: None
"""
assert client_config.hp_tuning
assert client_config.hp_tuning_config is not None
assert getattr(client_config.hp_tuning_config,
client_config.hp_tuning_config.param_1) is not None
assert getattr(client_config.hp_tuning_config,
client_config.hp_tuning_config.param_2) is not None
summary_results = []
for p_1 in getattr(client_config.hp_tuning_config,
client_config.hp_tuning_config.param_1):
for p_2 in getattr(client_config.hp_tuning_config,
client_config.hp_tuning_config.param_2):
time_str = str(time.time())
if client_config.logger is not None:
client_config.logger.info(
"Starting Hyperparameter tuning with p_1: {}, eps_decay: {}"
.format(p_1, p_2))
hparam_str = "{}={},{}={}".format(
client_config.hp_tuning_config.param_1, p_1,
client_config.hp_tuning_config.param_2, p_2)
create_dirs(client_config.output_dir + "/results/hpo", hparam_str)
try:
setattr(client_config.q_agent_config,
client_config.hp_tuning_config.param_1, p_1)
setattr(client_config.q_agent_config,
client_config.hp_tuning_config.param_2, p_2)
except:
try:
setattr(client_config.q_agent_config.dqn_config,
client_config.hp_tuning_config.param_1, p_1)
setattr(client_config.q_agent_config.dqn_config,
client_config.hp_tuning_config.param_2, p_2)
except:
try:
setattr(client_config.pg_agent_config,
client_config.hp_tuning_config.param_1, p_1)
setattr(client_config.pg_agent_config,
client_config.hp_tuning_config.param_2, p_2)
except Exception as e:
raise ValueError("Could not find hparams")
train_result, eval_result = Runner.run(client_config)
if client_config.logger is not None:
client_config.logger.info(
"Hyperparameter tuning with {}: {}, {}: {}, yielded hack prob:{}"
.format(client_config.hp_tuning_config.param_1, p_1,
client_config.hp_tuning_config.param_2, p_2,
eval_result.hack_probability[-1]))
if len(train_result.avg_episode_steps) > 0 and len(
eval_result.avg_episode_steps) > 0:
train_csv_path = client_config.output_dir + "/results/hpo/" + hparam_str + "/" + \
time_str + "_train" + ".csv"
train_result.to_csv(train_csv_path)
eval_csv_path = client_config.output_dir + "/results/hpo/" + hparam_str + "/" + time_str + "_eval" + ".csv"
eval_result.to_csv(eval_csv_path)
if client_config.q_agent_config is not None:
client_config.q_agent_config.to_csv(
client_config.output_dir + "/results/hpo/" +
hparam_str + "/hparams_" + time_str + ".csv")
if client_config.pg_agent_config is not None:
client_config.pg_agent_config.to_csv(
client_config.output_dir + "/results/hpo/" +
hparam_str + "/hparams_" + time_str + ".csv")
summary_results.append([
p_1, p_2, eval_result.hack_probability[-1],
train_result.cumulative_hack_probability[-1]
])
file_name = client_config.output_dir + "/results/hpo/" + "grid_" + client_config.hp_tuning_config.param_1 + "_" \
+ client_config.hp_tuning_config.param_2 + "_summary.csv"
with open(file_name, "w") as f:
writer = csv.writer(f)
writer.writerow([
client_config.hp_tuning_config.param_1,
client_config.hp_tuning_config.param_2, "eval_hp", "cumulative_hp"
])
for row_d in summary_results:
writer.writerow(row_d)
defender_type=AgentType.MANUAL_DEFENSE.value,
mode=RunnerMode.MANUAL_DEFENDER.value,
output_dir=default_output_dir(),
title="AttackMaximalAttacker vs ManualDefender")
return client_config
def write_default_config(path: str = None) -> None:
"""
Writes the default configuration to a json file
:param path: the path to write the configuration to
:return: None
"""
if path is None:
path = default_config_path()
config = default_config()
util.write_config_file(config, path)
# Program entrypoint
if __name__ == '__main__':
args = util.parse_args(default_config_path())
if args.configpath is not None:
if not os.path.exists(args.configpath):
write_default_config()
config = util.read_config(args.configpath)
else:
config = default_config()
env = Runner.run(config)
