def main(learning_rate):
tf.reset_default_graph(
) # to avoid the conflict with the existing parameters, but this is not suggested for reuse parameters
graph = tf.get_default_graph()
#print(graph.get_operations())
env = PowerDynSimEnv(case_files_array, dyn_config_file, rl_config_file,
java_port)
#model = deepq.models.mlp([128,128])
act = deepq.learn(env,
network=models.mlp(num_layers=2,
num_hidden=128,
activation=tf.nn.relu),
lr=learning_rate,
total_timesteps=900000,
buffer_size=50000,
checkpoint_freq=1000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
print_freq=10,
callback=callback)
print("Saving final model to power_model_multistep498_508_lr_%s_90w.pkl" %
(str(learning_rate)))
act.save(savedModel + "/" + model_name + "_lr_%s_90w.pkl" %
(str(learning_rate)))
case_files_array.append(folder_dir +
'\\testData\\Kundur-2area\\kunder_2area_ver30.raw')
case_files_array.append(folder_dir +
'\\testData\\Kundur-2area\\kunder_2area.dyr')
dyn_config_file = folder_dir + '\\testData\\Kundur-2area\\json\\kundur2area_dyn_config.json'
# rl_config_file = 'testData\\Kundur-2area\\json\\kundur2area_RL_config.json'
rl_config_file = folder_dir + '\\testData\\Kundur-2area\\json\\kundur2area_RL_config_multiStepObsv.json'
action_levels = [2]
import os.path
import sys
# This is to fix the issue of "ModuleNotFoundError" below
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from PowerDynSimEnvDef_v3 import PowerDynSimEnv
env = PowerDynSimEnv(case_files_array, dyn_config_file, rl_config_file,
java_port, action_levels)
for i in range(5):
results = env.step(1)
print('states =', results[0])
print('step reward =', results[1])
print('test completed')
env.close_connection()
print('connection with Ipss Server is closed')
step_durationtime = list()
check_pt_dir = "./PowerGridModels"
if not os.path.exists(check_pt_dir):
os.makedirs(check_pt_dir)
model_file = os.path.join(check_pt_dir, "gridmodel")
import time
start = time.time()
dataname = "multistep_obs11_randftd3_randbus3_3motor2action_prenull_100w"
action_cnts = [2, 2, 2]
env = PowerDynSimEnv(case_files_array,
dyn_config_file,
rl_config_file,
java_port,
cnts=action_cnts)
#for ll in [0.0001, 0.0005, 0.00005]:
for ll in [0.00005]:
step_rewards = list()
step_actions = list()
step_observations = list()
step_status = list()
step_starttime = list()
step_durationtime = list()
env.reset()
#model_path = "./previous_model/IEEE39_multistep_p150_3motor3action_prenull_008_lr_0.0001_30w.pkl"
case_files_array = []
case_files_array.append(folder_dir +
'\\testData\\Kundur-2area\\kunder_2area_ver30.raw')
case_files_array.append(folder_dir +
'\\testData\\Kundur-2area\\kunder_2area.dyr')
dyn_config_file = folder_dir + '\\testData\\Kundur-2area\\json\\kundur2area_dyn_config.json'
rl_config_file = folder_dir + '\\testData\\Kundur-2area\\json\\kundur2area_RL_config_multiStepObsv.json'
java_port = 25333
action_levels = [2]
env = PowerDynSimEnv(case_files_array, dyn_config_file, rl_config_file,
java_port, action_levels)
storedData = "./storedData581to585"
savedModel = "./previous_model"
model_name = "power_model_multistep_" + storedData[-8:]
def callback(lcl, glb):
# stop training if reward exceeds -30
is_solved = lcl['t'] > 100 and sum(
lcl['episode_rewards'][-101:-1]) / 100 >= -30.0
return is_solved
# episodes = 0
ipss_app = gateway.entry_point
# case_files = ['testData\\Kundur-2area\\kunder_2area_ver30.raw','testData\\Kundur-2area\\kunder_2area.dyr']
# Need to use the following way to define a String array in Python for Py4J
case_files_array = gateway.new_array(gateway.jvm.String, 2)
case_files_array[
0] = folder_dir + '\\' + 'testData\\IEEE39\\IEEE39bus_multiloads_xfmr4_smallX_v30.raw'
case_files_array[1] = folder_dir + '\\' + 'testData\\IEEE39\\IEEE39bus_3AC.dyr'
dyn_config_file = folder_dir + '\\' + 'testData\\IEEE39\\json\\IEEE39_dyn_config_v0.65.json'
rl_config_file = folder_dir + '\\' + 'testData\\IEEE39\\json\\IEEE39_RL_loadShedding_3motor_3levels_v0.65.json'
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from PowerDynSimEnvDef_v3 import PowerDynSimEnv
actions = [3, 3, 3]
env = PowerDynSimEnv(case_files_array,
dyn_config_file,
rl_config_file,
cnts=actions)
import numpy as np
manual_test()
env.close_connection()
print('test completed')