def train_attacker(
config: ClientConfig) -> Union[ExperimentResult, ExperimentResult]:
"""
Trains an attacker agent in the environment
:param config: Training configuration
:return: trainresult, evalresult
"""
env: IdsGameEnv = None
env = gym.make(config.env_name,
idsgame_config=config.idsgame_config,
save_dir=config.output_dir + "/results/data/" +
str(config.random_seed),
initial_state_path=config.initial_state_path)
if config.title is not None:
env.idsgame_config.render_config.title = config.title
attacker: TrainAgent = None
if config.attacker_type == AgentType.TABULAR_Q_AGENT.value:
attacker = TabularQAgent(env, config.q_agent_config)
elif config.attacker_type == AgentType.DQN_AGENT.value:
attacker = DQNAgent(env, config.q_agent_config)
elif config.attacker_type == AgentType.REINFORCE_AGENT.value:
attacker = ReinforceAgent(env, config.pg_agent_config)
elif config.attacker_type == AgentType.ACTOR_CRITIC_AGENT.value:
attacker = ActorCriticAgent(env, config.pg_agent_config)
elif config.attacker_type == AgentType.BAYES_ACTOR_CRITIC_AGENT.value:
attacker = BayesActorCriticAgent(env, config.pg_agent_config)
elif config.attacker_type == AgentType.PPO_AGENT.value:
attacker = PPOAgent(env, config.pg_agent_config)
elif config.attacker_type == AgentType.PPO_OPENAI_AGENT.value:
wrapper_env = BaselineEnvWrapper(
config.env_name,
idsgame_config=config.idsgame_config,
save_dir=config.output_dir + "/results/data/" +
str(config.random_seed),
initial_state_path=config.initial_state_path,
pg_agent_config=config.pg_agent_config)
if config.title is not None:
wrapper_env.idsgame_env.idsgame_config.render_config.title = config.title
attacker = OpenAiPPOAgent(wrapper_env, config.pg_agent_config)
else:
raise AssertionError(
"Attacker train agent type not recognized: {}".format(
config.attacker_type))
attacker.train()
train_result = attacker.train_result
eval_result = attacker.eval_result
return train_result, eval_result
alpha=0.00001,
epsilon=1,
render=False,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=100,
train_log_frequency=100,
epsilon_decay=0.9999,
video=True,
eval_log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/results/videos/" + str(random_seed),
num_episodes=20001,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs/" + str(random_seed),
eval_frequency=1000,
attacker=False,
defender=True,
video_frequency=101,
save_dir=default_output_dir() + "/results/data/" + str(random_seed),
dqn_config=dqn_config,
checkpoint_freq=5000)
env_name = "idsgame-maximal_attack-v3"
env = gym.make(env_name,
save_dir=default_output_dir() + "/results/data/" +
str(random_seed))
defender_agent = DQNAgent(env, q_agent_config)
defender_agent.train()
train_result = defender_agent.train_result
eval_result = defender_agent.eval_result