def hp_tuning_config(client_config: ClientConfig) -> ClientConfig:
"""
Setup config for hparam tuning
:param client_config: the client config
:return: the updated client config
"""
client_config.hp_tuning = True
client_config.hp_tuning_config = HpTuningConfig(param_1="alpha", param_2="num_hidden_layers",
alpha=[0.000001, 0.00001, 0.0001, 0.001, 0.01],
num_hidden_layers=[1, 2, 4, 8, 16])
client_config.run_many = False
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(gamma=0.999, alpha_attacker=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",
num_episodes=350001,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=10000, attacker=True, defender=False, video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000, input_dim_attacker=88, output_dim_attacker=80, hidden_dim=64,
num_hidden_layers=1, batch_size=32,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1)
env_name = "idsgame-minimal_defense-v7"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.ACTOR_CRITIC_AGENT.value,
mode=RunnerMode.TRAIN_ATTACKER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="Actor-Critic vs DefendMinimalDefender",
run_many=False, random_seeds=[0, 999, 299, 399, 499])
#client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
q_agent_config = QAgentConfig(gamma=0.9,
alpha=0.05,
epsilon=1,
render=False,
eval_sleep=0.9,
min_epsilon=0.1,
eval_episodes=100,
train_log_frequency=10,
epsilon_decay=0.999,
video=True,
eval_log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/videos",
num_episodes=5000,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/gifs",
eval_frequency=1000,
attacker=True,
defender=False,
video_frequency=101,
save_dir=default_output_dir() + "/data")
env_name = "idsgame-random_defense-v1"
client_config = ClientConfig(env_name=env_name,
attacker_type=AgentType.TABULAR_Q_AGENT.value,
mode=RunnerMode.TRAIN_ATTACKER.value,
q_agent_config=q_agent_config,
output_dir=default_output_dir(),
title="TrainingQAgent vs RandomDefender")
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
simulation_config = SimulationConfig(
render=True,
sleep=0.8,
video=True,
log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/videos",
num_episodes=1000,
gifs=True,
gif_dir=default_output_dir() + "/gifs",
video_frequency=1)
q_agent_config = QAgentConfig(attacker_load_path=default_output_dir() +
"/q_table/attacker_q_table.npy",
defender_load_path=default_output_dir() +
"/q_table/defender_q_table.npy")
env_name = "idsgame-v3"
client_config = ClientConfig(
env_name=env_name,
attacker_type=AgentType.TABULAR_Q_AGENT.value,
defender_type=AgentType.TABULAR_Q_AGENT.value,
mode=RunnerMode.SIMULATE.value,
simulation_config=simulation_config,
output_dir=default_output_dir(),
title="TabularQAgentAttacker vs TabularQAgentDefender",
q_agent_config=q_agent_config,
initial_state_path=default_output_dir() +
"/initial_state/initial_state.pkl")
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
simulation_config = SimulationConfig(
render=False,
sleep=0.8,
video=True,
log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/videos",
num_episodes=1000,
gifs=True,
gif_dir=default_output_dir() + "/gifs",
video_frequency=1)
env_name = "idsgame-v3"
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(),
title="RandomAttacker vs DefendMinimalDefender")
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(gamma=0.999, alpha_attacker=0.001, 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",
num_episodes=2200001,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=1000, attacker=True, defender=False, video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000, input_dim_attacker=(4 + 2) * 3 * 1, output_dim_attacker=4 * 3,
hidden_dim=32,
num_hidden_layers=1, batch_size=8,
gpu=True, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1, normalize_features=False, merged_ad_features=True,
zero_mean_features=False, gpu_id=0)
#input_dim = (4 + 3) * 2, output_dim_attacker = 4 * 2,
env_name = "idsgame-random_defense-v14"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.REINFORCE_AGENT.value,
mode=RunnerMode.TRAIN_ATTACKER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="TrainingREINFORCEAgent vs RandomDefender",
run_many=False, random_seeds=[0, 999, 299, 399, 499])
client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
dqn_config = DQNConfig(input_dim=242, defender_output_dim=242, attacker_output_dim=220, hidden_dim=64,
replay_memory_size=10000,
num_hidden_layers=1,
replay_start_size=1000, batch_size=32, target_network_update_freq=1000,
gpu=True, tensorboard=True, tensorboard_dir=default_output_dir() + "/results/tensorboard",
loss_fn="Huber", optimizer="Adam", lr_exp_decay=True, lr_decay_rate=0.9999)
q_agent_config = QAgentConfig(gamma=0.999, 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", num_episodes=20001,
eval_render=False, gifs=True, gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=1000, attacker=True, defender=True, video_frequency=101,
save_dir=default_output_dir() + "/results/data", dqn_config=dqn_config,
checkpoint_freq=5000)
env_name = "idsgame-v4"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.DQN_AGENT.value,
defender_type=AgentType.DQN_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER_AND_ATTACKER.value,
q_agent_config=q_agent_config, output_dir=default_output_dir(),
title="TrainingDQNAgent vs TrainingDQNAgent",
run_many=True, random_seeds=[0, 999, 299, 399, 499])
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
q_agent_config = QAgentConfig(gamma=0.999, alpha=0.0005, 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", num_episodes=20001,
eval_render=False, gifs=True, gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=1000, attacker=False, defender=True,
video_frequency=101,
save_dir=default_output_dir() + "/results/data")
env_name = "idsgame-maximal_attack-v8"
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(),
title="AttackMaximalAttacker vs TrainingQAgent",
run_many=True, random_seeds=[0, 999, 299, 399, 499])
# client_config.hp_tuning = True
# client_config.hp_tuning_config = HpTuningConfig(param_1="alpha", param_2="epsilon_decay",
# alpha=[0.0001, 0.001, 0.003, 0.05, 0.03],
# epsilon_decay=[0.99999, 0.9999, 0.999, 0.99, 0.9])
# client_config.run_many = False
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-minimal_defense-v18"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.MANUAL_ATTACK.value,
mode=RunnerMode.MANUAL_ATTACKER.value, output_dir=default_output_dir(),
title="ManualAttacker vs DefendMinimalDefender")
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(gamma=1, alpha_attacker=0.0001, epsilon=1, render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01, eval_episodes=1000, train_log_frequency=1,
epsilon_decay=0.9999, video=True, eval_log_frequency=500,
video_fps=5, video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=550000000, attacker=False, defender=True,
video_frequency=1001,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=100,
input_dim_attacker=((4 + 2) * 4),
output_dim_attacker=(4 + 1) * 4,
input_dim_defender=((4 + 1) * 4),
output_dim_defender=5 * 4,
hidden_dim=128, num_hidden_layers=2,
pi_hidden_layers=1, pi_hidden_dim=128, vf_hidden_layers=1,
vf_hidden_dim=128,
batch_size=2000,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1, normalize_features=False, merged_ad_features=True,
zero_mean_features=False, gpu_id=0, lstm_network=False,
lstm_seq_length=4, num_lstm_layers=2, optimization_iterations=10,
eps_clip=0.2, max_gradient_norm=0.5, gae_lambda=0.95,
cnn_feature_extractor=False, features_dim=512,
flatten_feature_planes=False, cnn_type=5, vf_coef=0.5, ent_coef=0.001,
render_attacker_view=False, lr_progress_power_decay=4,
lr_progress_decay=True, use_sde=False, sde_sample_freq=4,
one_hot_obs=False, lstm_core=False, lstm_hidden_dim=32,
multi_channel_obs=False,
channel_1_dim=32, channel_1_layers=2, channel_1_input_dim=16,
channel_2_dim=32, channel_2_layers=2, channel_2_input_dim=16,
channel_3_dim=32, channel_3_layers=2, channel_3_input_dim=4,
channel_4_dim=32, channel_4_layers=2, channel_4_input_dim=4,
mini_batch_size=64, ar_policy=True,
attacker_node_input_dim=((4 + 2) * 4),
attacker_at_net_input_dim=(4 + 2), attacker_at_net_output_dim=(4 + 1),
attacker_node_net_output_dim=4,
defender_node_input_dim=((4+1)*4), defender_at_net_input_dim=(4+1),
defender_node_net_output_dim=4, defender_at_net_output_dim=5)
env_name = "idsgame-maximal_attack-v20"
client_config = ClientConfig(env_name=env_name,
defender_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="AttackMaximalAttacker vs OpenAI-PPO",
run_many=False, random_seeds=[0, 999, 299, 399, 499], random_seed=999)
# client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-random_attack-v4"
client_config = ClientConfig(env_name=env_name, defender_type=AgentType.MANUAL_DEFENSE.value,
mode=RunnerMode.MANUAL_DEFENDER.value, output_dir=default_output_dir(),
title="RandomAttacker vs ManualDefender")
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-v14"
pg_agent_config = PolicyGradientAgentConfig(
gamma=0.999,
alpha_attacker=0.001,
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",
num_episodes=10000,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=1000,
attacker=True,
defender=False,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=1000,
input_dim_attacker=(4 + 3) * 2,
output_dim_attacker=4 * 2,
hidden_dim=32,
num_hidden_layers=1,
batch_size=8,
gpu=True,
tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam",
lr_exp_decay=False,
lr_decay_rate=0.999,
state_length=1,
normalize_features=False,
merged_ad_features=True,
zero_mean_features=False,
gpu_id=0,
attacker_load_path=
"/Users/kimham/workspace/rl/gym-idsgame/experiments/training/v14/minimal_defense/reinforce/results/data/0/1589370657.761538_attacker_policy_network.pt"
)
client_config = ClientConfig(env_name=env_name,
attacker_type=AgentType.REINFORCE_AGENT.value,
mode=RunnerMode.MANUAL_DEFENDER.value,
output_dir=default_output_dir(),
title="REINFORCE vs ManualDefender",
pg_agent_config=pg_agent_config,
bot_attacker=True)
return client_config
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 default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
opponent_pool_config = OpponentPoolConfig(pool_maxsize=100000,
pool_increment_period=300,
head_to_head_period=1,
quality_scores=True,
quality_score_eta=0.01,
initial_quality=1000,
pool_prob=0.5)
pg_agent_config = PolicyGradientAgentConfig(gamma=1, alpha_attacker=0.000008, epsilon=1, render=False,
alpha_defender=0.000003,
eval_sleep=0.9,
min_epsilon=0.01, eval_episodes=1000, train_log_frequency=1,
epsilon_decay=0.9999, video=True, eval_log_frequency=500,
video_fps=5, video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=175000, attacker=True, defender=True,
video_frequency=1001,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=250,
input_dim_attacker=((4 + 2) * 2)*1,
output_dim_attacker=(4 + 1) * 2,
input_dim_defender=((4 + 1) * 3)*1,
output_dim_defender=5 * 3,
hidden_dim=64,
num_hidden_layers=4, batch_size=2000,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1, normalize_features=False, merged_ad_features=True,
zero_mean_features=False, gpu_id=0, lstm_network=False,
lstm_seq_length=4, num_lstm_layers=2, optimization_iterations=10,
eps_clip=0.2, max_gradient_norm=0.5, gae_lambda=0.95,
cnn_feature_extractor=False, features_dim=512,
flatten_feature_planes=False, cnn_type=5, vf_coef=0.5, ent_coef=0.01,
render_attacker_view=False, lr_progress_power_decay=4,
lr_progress_decay=True, use_sde=False, sde_sample_freq=4,
opponent_pool=True, opponent_pool_config=opponent_pool_config,
alternating_optimization=True, alternating_period=300,
baselines_in_pool=True)
env_name = "idsgame-v18"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.PPO_OPENAI_AGENT.value,
defender_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER_AND_ATTACKER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="OpenAI-PPO vs OpenAI-PPO",
run_many=False, random_seeds=[0, 999, 299, 399, 499])
# client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-v19"
opponent_pool_config = OpponentPoolConfig(pool_maxsize=100000,
pool_increment_period=50,
head_to_head_period=1,
quality_scores=True,
quality_score_eta=0.01,
initial_quality=1000,
pool_prob=0.5)
pg_agent_config = PolicyGradientAgentConfig(gamma=1, alpha_attacker=0.00001, epsilon=1, render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01, eval_episodes=100, train_log_frequency=1,
epsilon_decay=0.9999, video=True, eval_log_frequency=1,
video_fps=5, video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=100000, attacker=True, defender=False,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000,
input_dim_attacker=(4 + 2) * 2,
output_dim_attacker=(4+1) * 2,
input_dim_defender=(4 + 2) * 3,
output_dim_defender=5 * 3,
hidden_dim=64,
num_hidden_layers=4, batch_size=2000,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1, normalize_features=False, merged_ad_features=True,
zero_mean_features=False, gpu_id=0, lstm_network=False,
lstm_seq_length=4, num_lstm_layers=2, optimization_iterations=10,
eps_clip=0.2, max_gradient_norm=0.5, gae_lambda=0.95,
cnn_feature_extractor=False, features_dim=512,
flatten_feature_planes=False,
attacker_load_path="/home/kim/storage/workspace/gym-idsgame/experiments/manual_play/v19/minimal_defense/manual_vs_openai_ppo/v4/1591164917.874881_attacker_policy_network.zip",
ar_policy=True, attacker_node_input_dim=((4 + 2) * 4),
attacker_at_net_input_dim=(4 + 2), attacker_at_net_output_dim=(4 + 1),
attacker_node_net_output_dim=4
)
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.MANUAL_DEFENDER.value, output_dir=default_output_dir(),
title="OpenAI PPO vs ManualDefender", pg_agent_config=pg_agent_config,
bot_attacker=True)
return client_config
def setup_train(config: ClientConfig, random_seed):
time_str = str(time.time())
util.create_artefact_dirs(config.output_dir, random_seed)
logger = util.setup_logger("tabular_q_vs_random_defense-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.q_agent_config.dqn_config.tensorboard_dir = default_output_dir() + "/results/tensorboard/" \
# + 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")
return time_str
def setup_agent_defender() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
q_agent_config = QAgentConfig(
gamma=0.999,
alpha=0.0005,
epsilon=1,
render=False,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=100,
train_log_frequency=50,
epsilon_decay=0.9999,
video=True,
eval_log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/results/videos",
num_episodes=5001,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=1000,
attacker=False,
defender=True,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
a_mu0=-10,
a_lambda0=4.0,
a_alpha0=3.0,
a_beta0=3.0,
num_mixture_samples=1000)
# env_name = "idsgame-random_defense-v3"
env_name = "idsgame-maximal_attack-v3"
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(),
title="TrainingQAgent vs DefendMinimalDefender",
run_many=True,
random_seeds=[0, 999, 299])
env = gym.make(client_config.env_name,
idsgame_config=client_config.idsgame_config,
save_dir=client_config.output_dir + "/results/data/" +
str(client_config.random_seed),
initial_state_path=client_config.initial_state_path)
defender = BQLAgent(env, client_config.q_agent_config)
return defender, client_config, env
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(
gamma=0.999,
alpha_attacker=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",
num_episodes=200001,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=10000,
attacker=False,
defender=True,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000,
input_dim_attacker=33,
output_dim_defender=33,
hidden_dim=64,
num_hidden_layers=1,
batch_size=32,
gpu=False,
tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam",
lr_exp_decay=False,
lr_decay_rate=0.999)
env_name = "idsgame-maximal_attack-v8"
client_config = ClientConfig(env_name=env_name,
defender_type=AgentType.REINFORCE_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER.value,
pg_agent_config=pg_agent_config,
output_dir=default_output_dir(),
title="AttackMaximalAttacker vs REINFORCE",
run_many=False,
random_seeds=[0, 999, 299, 399, 499])
client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
opponent_pool_config = OpponentPoolConfig(pool_maxsize=100000,
pool_increment_period=50,
head_to_head_period=1,
quality_scores=True,
quality_score_eta=0.01,
initial_quality=1000,
pool_prob=0.5)
pg_agent_config = PolicyGradientAgentConfig(gamma=0.999, alpha_attacker=0.0001, alpha_defender=0.0001,
epsilon=1, render=False, eval_sleep=0.9,
min_epsilon=0.01, eval_episodes=100, train_log_frequency=1,
epsilon_decay=0.9999, video=True, eval_log_frequency=1,
video_fps=5, video_dir=default_output_dir() + "/results/videos",
num_episodes=1350001,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=20000, attacker=True, defender=True, video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=100,
input_dim_attacker=((4 + 2) * 4),
output_dim_attacker=(4 + 1) * 4,
input_dim_defender=((4 + 1) * 4),
output_dim_defender=5 * 4,
hidden_dim=128,
num_hidden_layers=2, batch_size=64,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=True, lr_decay_rate=0.999,
state_length=1, alternating_optimization=True,
alternating_period=500, opponent_pool=True,
opponent_pool_config=opponent_pool_config,
normalize_features=False, merged_ad_features=True,
zero_mean_features=False)
env_name = "idsgame-v19"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.ACTOR_CRITIC_AGENT.value,
defender_type=AgentType.ACTOR_CRITIC_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER_AND_ATTACKER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="Actor-Critic vs Actor-Critic",
run_many=False, random_seeds=[0, 999, 299, 399, 499],
random_seed=0)
#client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
q_agent_config = QAgentConfig(
gamma=0.999,
alpha=0.0005,
epsilon=1,
render=False,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=100,
train_log_frequency=100,
epsilon_decay=0.999,
video=True,
eval_log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/results/videos",
num_episodes=20001,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=2000,
attacker=True,
defender=False,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
tab_full_state_space=True)
env_name = "idsgame-minimal_defense-v13"
client_config = ClientConfig(
env_name=env_name,
attacker_type=AgentType.TABULAR_Q_AGENT.value,
mode=RunnerMode.TRAIN_ATTACKER.value,
q_agent_config=q_agent_config,
output_dir=default_output_dir(),
title="TrainingQAgent vs DefendMinimalDefender",
run_many=False,
random_seeds=[0, 999, 299, 399, 499])
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(gamma=1, alpha_attacker=0.0001, epsilon=1, render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01, eval_episodes=100, train_log_frequency=1,
epsilon_decay=0.9999, video=True, eval_log_frequency=1,
video_fps=5, video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False, gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=25000, attacker=False, defender=True,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000, input_dim_attacker=(4 + 1) * 3,
output_dim_attacker=4 * 3,
input_dim_defender=(4 + 1) * 3,
output_dim_defender=5 * 3,
hidden_dim=32,
num_hidden_layers=2, batch_size=2000,
gpu=False, tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam", lr_exp_decay=False, lr_decay_rate=0.999,
state_length=1, normalize_features=False, merged_ad_features=False,
zero_mean_features=False, gpu_id=0, lstm_network=False,
lstm_seq_length=4, num_lstm_layers=2, optimization_iterations=10,
eps_clip=0.2, max_gradient_norm=0.5, gae_lambda=0.95)
env_name = "idsgame-maximal_attack-v17"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.PPO_OPENAI_AGENT.value,
defender_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.TRAIN_DEFENDER.value,
pg_agent_config=pg_agent_config, output_dir=default_output_dir(),
title="AttackMaximalAttacker vs OpenAI-PPO",
run_many=False, random_seeds=[0, 999, 299, 399, 499])
# client_config = hp_tuning_config(client_config)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-v19"
pg_agent_config = PolicyGradientAgentConfig(
gamma=1,
alpha_attacker=0.0001,
epsilon=1,
render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=1000,
train_log_frequency=1,
epsilon_decay=0.9999,
video=True,
eval_log_frequency=500,
video_fps=5,
video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=55000,
attacker=False,
defender=True,
video_frequency=1001,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=250,
input_dim_attacker=((4 + 2) * 4),
output_dim_attacker=(4 + 1) * 4,
input_dim_defender=((4 + 1) * 4),
output_dim_defender=5 * 4,
hidden_dim=128,
num_hidden_layers=2,
pi_hidden_layers=1,
pi_hidden_dim=128,
vf_hidden_layers=1,
vf_hidden_dim=128,
batch_size=2000,
gpu=False,
tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam",
lr_exp_decay=False,
lr_decay_rate=0.999,
state_length=1,
normalize_features=False,
merged_ad_features=True,
zero_mean_features=False,
gpu_id=0,
lstm_network=False,
lstm_seq_length=4,
num_lstm_layers=2,
optimization_iterations=10,
eps_clip=0.2,
max_gradient_norm=0.5,
gae_lambda=0.95,
cnn_feature_extractor=False,
features_dim=512,
flatten_feature_planes=False,
cnn_type=5,
vf_coef=0.5,
ent_coef=0.001,
render_attacker_view=False,
lr_progress_power_decay=4,
lr_progress_decay=True,
use_sde=False,
sde_sample_freq=4,
one_hot_obs=False,
lstm_core=False,
lstm_hidden_dim=32,
multi_channel_obs=False,
channel_1_dim=32,
channel_1_layers=2,
channel_1_input_dim=16,
channel_2_dim=32,
channel_2_layers=2,
channel_2_input_dim=16,
channel_3_dim=32,
channel_3_layers=2,
channel_3_input_dim=4,
channel_4_dim=32,
channel_4_layers=2,
channel_4_input_dim=4,
mini_batch_size=64,
ar_policy=True,
attacker_node_input_dim=((4 + 2) * 4),
attacker_at_net_input_dim=(4 + 2),
attacker_at_net_output_dim=(4 + 1),
attacker_node_net_output_dim=4,
defender_node_input_dim=((4 + 1) * 4),
defender_at_net_input_dim=(4 + 1),
defender_node_net_output_dim=4,
defender_at_net_output_dim=5,
defender_load_path=
"/home/kim/workspace/gym-idsgame/experiments/manual_play/v19/maximal_attack/manual_vs_openai_ppo/1592125075.4390159_defender_node_policy_network.zip"
)
client_config = ClientConfig(
env_name=env_name,
defender_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.MANUAL_ATTACKER.value,
output_dir=default_output_dir(),
title="ManualAttacker vs OpenAI PPO",
pg_agent_config=pg_agent_config,
bot_defender=True)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
env_name = "idsgame-v16"
pg_agent_config = PolicyGradientAgentConfig(
gamma=1,
alpha_attacker=0.00001,
epsilon=1,
render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=100,
train_log_frequency=1,
epsilon_decay=0.9999,
video=True,
eval_log_frequency=1,
video_fps=5,
video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=100000,
attacker=True,
defender=False,
video_frequency=101,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=5000,
input_dim_attacker=(4 + 2) * 3,
output_dim_attacker=4 * 3,
input_dim_defender=(4 + 2) * 3,
output_dim_defender=5 * 3,
hidden_dim=64,
num_hidden_layers=4,
batch_size=2000,
gpu=False,
tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam",
lr_exp_decay=False,
lr_decay_rate=0.999,
state_length=1,
normalize_features=False,
merged_ad_features=True,
zero_mean_features=False,
gpu_id=1,
lstm_network=False,
lstm_seq_length=4,
num_lstm_layers=2,
optimization_iterations=10,
eps_clip=0.2,
max_gradient_norm=0.5,
gae_lambda=0.95,
cnn_feature_extractor=False,
features_dim=512,
flatten_feature_planes=False,
attacker_load_path=
"/Users/kimham/workspace/rl/gym-idsgame/experiments/manual_play/v16/minimal_defense/manual_vs_openai_ppo/1590564756.5454807_attacker_policy_network.zip"
)
client_config = ClientConfig(
env_name=env_name,
attacker_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.MANUAL_DEFENDER.value,
output_dir=default_output_dir(),
title="OpenAI PPO vs ManualDefender",
pg_agent_config=pg_agent_config,
bot_attacker=True)
return client_config
def default_config() -> ClientConfig:
"""
:return: Default configuration for the experiment
"""
pg_agent_config = PolicyGradientAgentConfig(
gamma=1,
alpha_attacker=0.00003,
epsilon=1,
render=False,
alpha_defender=0.0001,
eval_sleep=0.9,
min_epsilon=0.01,
eval_episodes=1000,
train_log_frequency=1,
epsilon_decay=0.9999,
video=True,
eval_log_frequency=500,
video_fps=5,
video_dir=default_output_dir() + "/results/videos",
num_episodes=100000000,
eval_render=False,
gifs=True,
gif_dir=default_output_dir() + "/results/gifs",
eval_frequency=175000,
attacker=True,
defender=False,
video_frequency=1001,
save_dir=default_output_dir() + "/results/data",
checkpoint_freq=250,
input_dim_attacker=((4 + 2) * 2),
output_dim_attacker=(4 + 1) * 2,
input_dim_defender=((4 + 1) * 3),
output_dim_defender=5 * 3,
hidden_dim=32,
num_hidden_layers=2,
batch_size=2000,
gpu=True,
tensorboard=True,
tensorboard_dir=default_output_dir() + "/results/tensorboard",
optimizer="Adam",
lr_exp_decay=False,
lr_decay_rate=0.999,
state_length=1,
normalize_features=False,
merged_ad_features=True,
zero_mean_features=False,
gpu_id=0,
lstm_network=False,
lstm_seq_length=4,
num_lstm_layers=2,
optimization_iterations=10,
eps_clip=0.2,
max_gradient_norm=0.5,
gae_lambda=0.95,
cnn_feature_extractor=False,
features_dim=512,
flatten_feature_planes=False,
cnn_type=5,
vf_coef=0.5,
ent_coef=0.00,
render_attacker_view=True,
lr_progress_power_decay=4,
lr_progress_decay=True,
use_sde=False,
sde_sample_freq=4,
one_hot_obs=False)
# input_dim_attacker = (3, 3, 5),
# output_dim_attacker = (5 * 2) * 3,
# input_dim_defender = (3, 3, 5),
# output_dim_defender = 6 * 3,
# input_dim_attacker = ((5 * 2 + 1) * 3),
# output_dim_attacker = (5 * 2) * 3,
# input_dim_defender = ((5 + 1) * 3),
# output_dim_defender = 6 * 3,
env_name = "idsgame-minimal_defense-v18"
client_config = ClientConfig(
env_name=env_name,
attacker_type=AgentType.PPO_OPENAI_AGENT.value,
mode=RunnerMode.TRAIN_ATTACKER.value,
pg_agent_config=pg_agent_config,
output_dir=default_output_dir(),
title="OpenAI-PPO vs DefendMinimalDefender",
run_many=False,
random_seeds=[0, 999, 299, 399, 499])
# client_config = hp_tuning_config(client_config)
return client_config
