def __init__(self, configs):
super().__init__(configs)
if configs['mode'] == 'train' or configs['mode'] == 'simulate':
os.mkdir(
os.path.join(self.configs['current_path'], 'training_data',
self.configs['time_data'], 'model'))
self.configs = merge_dict(configs, DEFAULT_CONFIG)
else: # test
self.configs = merge_dict_non_conflict(configs, DEFAULT_CONFIG)
self.num_agent = len(self.configs['tl_rl_list'])
self.state_space = self.configs['state_space']
# action space
# rate action space
self.rate_action_space = self.configs['rate_action_space']
# time action space
self.time_action_space = self.configs['time_action_space']
self.action_size = self.configs['action_size']
self.gamma = self.configs['gamma']
self.epsilon = self.configs['epsilon']
self.criterion = nn.MSELoss()
self.lr = self.configs['lr']
self.lr_decay_rate = self.configs['lr_decay_rate']
self.epsilon_decay_rate = self.configs['epsilon_decay_rate']
self.batch_size = self.configs['batch_size']
self.device = self.configs['device']
self.running_loss = 0
# self.writer=writer
# NN composition
# size에 따라 다르게 해주어야함
self.rate_key_list = list()
for i, key in enumerate(self.configs['traffic_node_info'].keys()):
if configs['mode'] == 'train':
rate_key = self.configs['traffic_node_info'][key]['num_phase']
elif configs['mode'] == 'test':
rate_key = str(
self.configs['traffic_node_info'][key]['num_phase'])
self.rate_key_list.append(rate_key)
self.mainSuperQNetwork = SuperQNetwork(
self.state_space, self.rate_action_space[rate_key],
self.time_action_space[0], self.configs)
self.targetSuperQNetwork = SuperQNetwork(
self.state_space, self.rate_action_space[rate_key],
self.time_action_space[0], self.configs)
# hard update, optimizer setting
self.optimizer = optim.Adadelta(self.mainSuperQNetwork.parameters(),
lr=self.configs['lr'])
hard_update(self.targetSuperQNetwork, self.mainSuperQNetwork)
self.lr_scheduler = optim.lr_scheduler.StepLR(
optimizer=self.optimizer,
step_size=self.configs['lr_decay_period'],
gamma=self.configs['lr_decay_rate'])
def train(flags, time_data, configs, sumoConfig):
# check gui option
if flags.disp == True:
sumoBinary = checkBinary('sumo-gui')
else:
sumoBinary = checkBinary('sumo')
sumoCmd = [sumoBinary, "-c", sumoConfig, '--start']
# configs setting
configs['num_agent'] = len(configs['tl_rl_list'])
configs['algorithm'] = flags.algorithm.lower()
configs['randomness'] = flags.randomness
print("training algorithm: ", configs['algorithm'])
configs['action_size'] = 2
# state space 는 map.py에서 결정
if flags.network.lower() == 'grid':
configs['state_space'] = 10
configs['model'] = 'city'
from train import city_dqn_train
from configs import SUPER_DQN_TRAFFIC_CONFIGS
configs = merge_dict_non_conflict(configs, SUPER_DQN_TRAFFIC_CONFIGS)
city_dqn_train(configs, time_data, sumoCmd)
def main(args):
random_seed = 20000
random.seed(random_seed)
torch.manual_seed(random_seed)
np.random.seed(random_seed)
flags = parse_args(args)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda and flags.gpu == True else "cpu")
# device = torch.device('cpu')
print("Using device: {}".format(device))
configs = EXP_CONFIGS
configs['device'] = str(device)
configs['current_path'] = os.path.dirname(os.path.abspath(__file__))
configs['mode'] = flags.mode.lower()
time_data = time.strftime('%m-%d_%H-%M-%S', time.localtime(time.time()))
configs['time_data'] = str(time_data)
if os.path.exists(os.path.join(os.path.dirname(__file__), 'data')):
if os.path.exists(
os.path.join(os.path.dirname(__file__), 'data',
configs['mode'])) == False:
os.mkdir(
os.path.join(os.path.dirname(__file__), 'data',
configs['mode']))
configs['file_name'] = configs['time_data']
# check the network
configs['network'] = flags.network.lower()
if configs['network'] == 'grid':
from Network.grid import GridNetwork # network바꿀때 이걸로 바꾸세요(수정 예정)
configs['grid_num'] = 5
configs['scale'] = 1
if configs['mode'] == 'simulate':
configs['file_name'] = '{}x{}grid'.format(configs['grid_num'],
configs['grid_num'])
elif configs['mode'] == 'test': # test
configs['file_name'] = flags.replay_name.lower()
# Generating Network
network = GridNetwork(configs, grid_num=configs['grid_num'])
network.generate_cfg(True, configs['mode'])
NET_CONFIGS = network.get_configs()
configs = merge_dict_non_conflict(configs, NET_CONFIGS)
# Generating Network
else: # map file 에서 불러오기
print("Load from map file")
from Network.map import MapNetwork
# TODO Grid num은 삭제요망
configs['grid_num'] = 3
configs['num_lanes'] = 2
configs['load_file_name'] = configs['network']
mapnet = MapNetwork(configs)
MAP_CONFIGS = mapnet.get_tl_from_xml()
for key in MAP_CONFIGS.keys():
configs[key] = MAP_CONFIGS[key]
mapnet.gen_net_from_xml()
mapnet.gen_rou_from_xml()
mapnet.generate_cfg(True, configs['mode'])
mapnet._generate_add_xml()
if configs['network'] == '3x3grid':
configs['scale'] = str(1)
if configs['network'] == '5x5grid':
configs['scale'] = str(1)
if configs['network'] == '5x5grid_v2':
configs['scale'] = str(1.5)
if configs['network'] == 'dunsan':
configs['scale'] = str(1)
if configs['network'] == 'dunsan_v2':
configs['scale'] = str(0.8)
print("Scale:", configs['scale'])
# check the environment
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
sys.path.append(tools)
else:
sys.exit("please declare environment variable 'SUMO_HOME'")
# check the mode
if configs['mode'] == 'train':
# init train setting
configs['update_type'] = flags.update_type
sumoConfig = os.path.join(configs['current_path'], 'training_data',
time_data, 'net_data',
configs['file_name'] + '_train.sumocfg')
train(flags, time_data, configs, sumoConfig)
elif configs['mode'] == 'test':
configs['file_name'] = flags.replay_name
configs['replay_name'] = configs['time_data']
sumoConfig = os.path.join(configs['current_path'], 'training_data',
time_data, 'net_data',
configs['time_data'] + '_test.sumocfg')
test(flags, configs, sumoConfig)
else: # simulate
sumoConfig = os.path.join(configs['current_path'], 'Net_data',
configs['file_name'] + '_simulate.sumocfg')
simulate(flags, configs, sumoConfig)
def __init__(self, configs):
super().__init__(configs)
if configs['mode'] == 'train' or configs['mode'] == 'simulate':
os.mkdir(
os.path.join(self.configs['current_path'], 'training_data',
self.configs['time_data'], 'model'))
self.configs = merge_dict(configs, DEFAULT_CONFIG)
else: # test
self.configs = merge_dict_non_conflict(configs, DEFAULT_CONFIG)
self.num_agent = len(self.configs['tl_rl_list'])
self.state_space = self.configs['state_space']
# action space
# rate action space
self.rate_action_space = self.configs['rate_action_space']
# time action space
self.time_action_space = self.configs['time_action_space']
self.action_size = self.configs['action_size']
self.gamma = self.configs['gamma']
self.epsilon = self.configs['epsilon']
self.criterion = nn.SmoothL1Loss()
self.lr = self.configs['lr']
self.lr_decay_rate = self.configs['lr_decay_rate']
self.epsilon_decay_rate = self.configs['epsilon_decay_rate']
self.batch_size = self.configs['batch_size']
self.device = self.configs['device']
self.running_loss = 0
self.super_output_size = int(self.num_agent * 2)
self.super_input_size = int(self.num_agent)
# NN composition
self.mainSuperQNetwork = SuperQNetwork(self.super_input_size,
self.super_output_size,
self.configs)
self.targetSuperQNetwork = SuperQNetwork(self.super_input_size,
self.super_output_size,
self.configs)
# size에 따라 다르게 해주어야함
self.mainQNetwork = list()
self.targetQNetwork = list()
self.rate_key_list = list()
for i, key in enumerate(self.configs['traffic_node_info'].keys()):
if configs['mode'] == 'train':
rate_key = self.configs['traffic_node_info'][key]['num_phase']
elif configs['mode'] == 'test':
rate_key = str(
self.configs['traffic_node_info'][key]['num_phase'])
self.rate_key_list.append(rate_key)
self.mainQNetwork.append(
QNetwork(self.super_output_size,
self.rate_action_space[rate_key],
self.time_action_space[i], self.configs))
self.targetQNetwork.append(
QNetwork(self.super_output_size,
self.rate_action_space[rate_key],
self.time_action_space[i], self.configs))
# hard update, optimizer setting
self.optimizer = list()
hard_update(self.targetSuperQNetwork, self.mainSuperQNetwork)
for targetQ, mainQ in zip(self.targetQNetwork, self.mainQNetwork):
hard_update(targetQ, mainQ)
params = chain(self.mainSuperQNetwork.parameters(),
mainQ.parameters())
self.optimizer.append(optim.Adam(params, lr=self.lr))
# Network
print("========SUPER NETWORK==========\n", self.mainSuperQNetwork)
print("========NETWORK==========\n")
for i in range(self.num_agent):
print(self.mainQNetwork[i])