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_vi():
capacity= 20
predict_round=6000
gamma=0.6
replay_DhpVI = ReplayBuffer(capacity=capacity)
env_DhpVI = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new dhp_vi controller')
dhp_vi = DhpVI(
replay_buffer = replay_DhpVI,
u_bounds = env_DhpVI.u_bounds,
#exploration = None,
exploration = exploration,
env=env_DhpVI,
predict_training_rounds=predict_round,
gamma=gamma,
batch_size = 20,
predict_batch_size=32,
model_nn_error_limit = 0.0008,
critic_nn_error_limit = 0.01,
actor_nn_error_limit = 0.001,
# 0.005
actor_nn_lr = 0.005,
critic_nn_lr = 0.001,
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=2000,
Nc=100,
test_period=3,
max_u_iters=2000,
policy_visual_period=400,
img_path=EXP_NAME
)
env_DhpVI.reset()
dhp_vi.train_identification_model()
return dhp_vi
def new_dhp():
capacity= 1
predict_round=6000
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 = 1,
predict_batch_size=32,
model_nn_error_limit = 0.0008,
critic_nn_error_limit = 0.01,
actor_nn_error_limit = 0.001,
# 0.005
actor_nn_lr = 0.005,
critic_nn_lr = 0.001,
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=220,
Nc=100,
test_period=3,
img_path=EXP_NAME,
)
env_DHP.reset()
dhp.train_identification_model()
return dhp
def new_vi_ub():
capacity=2
predict_round=3000
u_optim='sgd'
replay_vi = ReplayBuffer(capacity=capacity)
env_VI = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new viuk controller')
vi = VIub(
replay_buffer = replay_vi,
u_bounds = env_VI.u_bounds,
#exploration = None,
exploration = exploration,
env=env_VI,
predict_training_rounds=predict_round,
gamma=0.6,
batch_size = capacity,
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,
u_optim=u_optim,
find_lr= 0.4,
find_time_max=20
)
env_VI.reset()
vi.train_identification_model()
return vi
def new_hdp():
predict_round=3000
gamma=0.6
replay_hdp = ReplayBuffer(capacity=2)
env_HDP = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new hdp controller')
hdp = HDP(
replay_buffer = replay_hdp,
u_bounds = env_HDP.u_bounds,
#exploration = None,
exploration = exploration,
env=env_HDP,
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 = 0.001,
# 0.005
actor_nn_lr = 0.003,
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_HDP.reset()
hdp.train_identification_model()
return hdp
def new_vi_sample(capacity=2, predict_round=3000):
replay_vi_sample = ReplayBuffer(capacity=capacity)
env_VI_sample = Thickener(
noise_p=0.03,
noise_in=True,
)
exploration = No_Exploration()
print('make new vi_sample controller')
vi_sample = ViSample(
replay_buffer=replay_vi_sample,
u_bounds=env_VI_sample.u_bounds,
#exploration = None,
exploration=exploration,
env=env_VI_sample,
predict_training_rounds=predict_round,
gamma=0.4,
batch_size=capacity,
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,
)
env_VI_sample.reset()
vi_sample.train_identification_model()
vi_sample.test_predict_model(test_rounds=100)
return vi_sample
y_low=[-15, -15],
y_high=[15, 15],
u_high=[2, 2],
u_low=[-2, -2],
reward_calculator=reward3,
normalize=False)
replay_buffer1 = ReplayBuffer(1000)
replay_buffer2 = ReplayBuffer(100)
exploration_noise1 = EGreedy(
action_bounds=env1.u_bounds,
epsilon_start=0.5,
epsilon_final=0.4,
epsilon_decay=100000,
)
exploration_noise1 = No_Exploration()
exploration_noise2 = GaussianExploration(
action_bounds=env2.external_u_bounds,
min_sigma=1.0,
max_sigma=1.01,
decay_period=100000)
controller1 = Td3(gpu_id=1,
num_inputs=env1.observation_size(),
num_actions=2,
act_hidden_size=16,
val_hidden_size=16,
replay_buffer=replay_buffer1,
u_bounds=env1.u_bounds,
exploration=exploration_noise1,
