def run_vi(
rounds=1000,
seed=random.randint(0, 1000000),
name='VI',
capacity=2,
predict_round=3000,
u_optim='adam',
):
print('seed :', seed)
torch.manual_seed(seed)
vi_para = {'gamma': 0.2}
vi = new_vi()
penalty = Quadratic(**penalty_para)
thickner_para['random_seed'] = seed
env_vi = Thickener(
penalty_calculator=penalty,
**thickner_para,
)
res1 = OneRoundExp(controller=vi,
env=env_vi,
max_step=rounds,
exp_name=name).run()
print(name, ':', vi.u_iter_times * 1.0 / rounds)
return res1
def run_dhp(
rounds=800,
seed=random.randint(0, 1000000),
name='DHP',
capacity=2,
predict_round=3000,
u_optim='adam',
):
seed = 8312279
print('seed :', seed)
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
dhp = new_dhp()
penalty = Quadratic(**penalty_para)
env_dhp = Thickener(
penalty_calculator=penalty,
**thickner_para,
)
res1 = OneRoundExp(controller=dhp,
env=env_dhp,
max_step=rounds,
exp_name=name).run()
return res1
def new_vi(capacity=2, batch_size=2):
capacity = capacity
predict_round=3000
u_optim='adam'
gamma=0.6
replay_vi = ReplayBuffer(capacity=capacity)
env_VI = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new vi controller')
vi = VI(
replay_buffer = replay_vi,
u_bounds = env_VI.u_bounds,
#exploration = None,
exploration = exploration,
env=env_VI,
predict_training_rounds=predict_round,
gamma=gamma,
batch_size = batch_size,
predict_batch_size=32,
model_nn_error_limit = 0.0008,
critic_nn_error_limit = 0.001,
actor_nn_error_limit = 0.001,
actor_nn_lr = 0.005,
critic_nn_lr = 0.01,
model_nn_lr = 0.01,
indice_y = None,
indice_y_star = None,
indice_c=None,
hidden_model = 10,
hidden_critic = 14,
hidden_actor = 14,
predict_epoch= 30,
Nc=1000,
u_optim=u_optim,
img_path=EXP_NAME
)
env_VI.reset()
vi.train_identification_model()
#vi.test_predict_model(test_rounds=100)
return vi
def new_dhp():
capacity = 3
predict_round = 3000
gamma = 0.6
replay_DHP = ReplayBuffer(capacity=capacity)
env_DHP = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new dhp controller')
dhp = DHP(
replay_buffer=replay_DHP,
u_bounds=env_DHP.u_bounds,
#exploration = None,
exploration=exploration,
env=env_DHP,
predict_training_rounds=predict_round,
gamma=gamma,
batch_size=2,
predict_batch_size=32,
model_nn_error_limit=0.0008,
critic_nn_error_limit=0.001,
actor_nn_error_limit=0.0001,
# 0.005
actor_nn_lr=0.008,
critic_nn_lr=0.01,
model_nn_lr=0.01,
indice_y=None,
indice_y_star=None,
indice_c=None,
hidden_model=10,
hidden_critic=12,
hidden_actor=14,
predict_epoch=30,
Na=200,
Nc=300,
img_path=EXP_NAME)
env_DHP.reset()
dhp.train_identification_model()
return dhp
def new_ILPL():
predict_round = 3000
gamma = 0.6
replay_ILPL = ReplayBuffer(capacity=4)
env_ILPL = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new ilpl controller')
ilpl = ILPL(
replay_buffer=replay_ILPL,
u_bounds=env_ILPL.u_bounds,
#exploration = None,
exploration=exploration,
env=env_ILPL,
predict_training_rounds=predict_round,
gamma=gamma,
batch_size=2,
predict_batch_size=32,
model_nn_error_limit=0.0008,
critic_nn_error_limit=0.1,
actor_nn_error_limit=0.001,
# 0.005
actor_nn_lr=0.001,
critic_nn_lr=0.02,
model_nn_lr=0.01,
indice_y=None,
indice_y_star=None,
indice_c=None,
hidden_model=10,
hidden_critic=14,
hidden_actor=14,
predict_epoch=30,
Na=220,
Nc=500,
img_path=EXP_NAME)
env_ILPL.reset()
ilpl.train_identification_model()
return ilpl
def run_ILPL(rounds=1000,
seed=random.randint(0, 1000000),
name='ILPL',
predict_round=800):
print('seed :', seed)
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
ilpl = new_ILPL()
penalty = Quadratic(**penalty_para)
env_ILPL = Thickener(
penalty_calculator=penalty,
**thickner_para,
)
res1 = OneRoundExp(controller=ilpl,
env=env_ILPL,
max_step=rounds,
exp_name=name).run()
return res1
def run_hdp(rounds=1000,
seed=random.randint(0, 1000000),
name='HDP',
predict_round=800):
print('seed :', seed)
hdp_para = {'gamma': 0.2}
torch.manual_seed(seed)
hdp = new_hdp()
penalty = Quadratic(**penalty_para)
thickner_para['random_seed'] = seed
env_hdp = Thickener(
penalty_calculator=penalty,
**thickner_para,
)
res1 = OneRoundExp(controller=hdp,
env=env_hdp,
max_step=rounds,
exp_name=name).run()
return res1