def state_update(self, obs, action):
# update history
if self.dst:
self.dst.state['history'].append([str(action)])
# NLU parsing
input_act = self.nlu.parse(
obs,
sum(self.dst.state['history'], [])
if self.dst else []) if self.nlu else obs
input_act = da_normalize(input_act, role='usr')
# state tracking
state = self.dst.update(input_act) if self.dst else input_act
# update history
if self.dst:
self.dst.state['history'][-1].append(str(obs))
# encode state
encoded_state = self.state_encoder.encode(
state) if self.state_encoder else state
if self.nlu and self.dst:
self.dst.state['user_action'] = input_act
elif self.dst and not isinstance(
self.dst, (word_dst.MDBTTracker,
word_dst.TRADETracker)): # for act-in act-out agent
self.dst.state['user_action'] = obs
logger.nl(f'User utterance: {obs}')
logger.act(f'Inferred user action: {input_act}')
logger.state(f'Dialog state: {state}')
return input_act, state, encoded_state
def step(self, action):
action = da_normalize(action, role='sys')
user_response, user_act, session_over, reward = self.simulator.response(
action, self.history)
self.last_act = user_act
self.history.extend([f'{action}', f'{user_response}'])
logger.act(f'Inferred system action: {self.get_sys_act()}')
# update evaluator
if self.evaluator:
self.evaluator.add_sys_da(self.get_sys_act())
self.evaluator.add_usr_da(self.get_last_act())
if session_over:
reward = 2.0 * self.simulator.policy.max_turn if self.evaluator.task_success(
) else -1.0 * self.simulator.policy.max_turn
else:
reward = -1.0
self.env_info = [State(user_response, reward, session_over)]
return self.env_info