def generate_necessary_file(root_dir):
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
vector = MultiWozVector(voc_file, voc_opp_file)
action_map_file = os.path.join(root_dir, 'convlab2/policy/act_500_list.txt')
act2ind_dict, ind2act_dict = read_action_map(action_map_file)
return vector, act2ind_dict, ind2act_dict
def __init__(self, train=True):
# load configuration file
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
cfg = json.load(f)
self.gamma = cfg['gamma']
self.epsilon_init = cfg['epsilon_init']
self.epsilon_final = cfg['epsilon_final']
self.istrain = train
if self.istrain:
self.epsilon = self.epsilon_init
else:
self.epsilon = self.epsilon_final
self.epsilon_degrade_period = cfg['epsilon_degrade_period']
self.tau = cfg['tau']
self.action_number = cfg[
'action_number'] # total number of actions considered
init_logging_handler(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
cfg['log_dir']))
# load action mapping file
action_map_file = os.path.join(root_dir,
'convlab2/policy/act_500_list.txt')
_, self.ind2act_dict = read_action_map(action_map_file)
# load vector for MultiWoz 2.1
voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
self.vector = MultiWozVector(voc_file, voc_opp_file)
# build Q network
# current Q network to be trained
self.Q = DuelDQN(self.vector.state_dim, cfg['h_dim'],
self.action_number).to(device=DEVICE)
# target Q network
self.target_Q = DuelDQN(self.vector.state_dim, cfg['h_dim'],
self.action_number).to(device=DEVICE)
self.target_Q.load_state_dict(self.Q.state_dict())
# define optimizer
# self.optimizer = RAdam(self.Q.parameters(), lr=cfg['lr'], weight_decay=cfg['weight_decay'])
self.optimizer = optim.Adam(self.Q.parameters(),
lr=cfg['lr'],
weight_decay=cfg['weight_decay'])
self.scheduler = StepLR(self.optimizer,
step_size=cfg['lr_decay_step'],
gamma=cfg['lr_decay'])
self.min_lr = cfg['min_lr']
# loss function
self.criterion = torch.nn.MSELoss()