maxbrenner-ai

def run_round(state, warmup=False):
   # 1) Agent takes action given state tracker's representation of dialogue (state)
   agent_action_index, agent_action = dqn_agent.get_action(state, use_rule=warmup)
   # 2) Update state tracker with the agent's action
   round_num = state_tracker.update_state_agent(agent_action)
   # 3) User takes action given agent action
   user_action, reward, done, success = user.step(agent_action, round_num)
   if not done:
       # 4) Infuse error into semantic frame level of user action
       emc.infuse_error(user_action)

maxbrenner-ai

def episode_reset():
   # First reset the state tracker
   state_tracker.reset()
   # Then pick an init user action
   user_action = user.reset()
   # Infuse with error
   emc.infuse_error(user_action)
   # And update state tracker
   state_tracker.update_state_user(user_action)
   # Finally, reset agent

maxbrenner-ai

def warmup_run():
  total_step = 0
  while total_step != WARMUP_MEM and not dqn_agent.is_memory_full():
     # Reset episode
     episode_reset()
     done = False
     # Get initial state from state tracker
     state = state_tracker.get_state()
     while not done:
         next_state, _, done, _ = run_round(state, warmup=True)

maxbrenner-ai

def train_run():
  episode = 0
  period_success_total = 0
  success_rate_best = 0.0
  # Almost exact same loop as warm-up -----
  while episode < NUM_EP_TRAIN:
     episode_reset()
     episode += 1
     done = False
     state = state_tracker.get_state()

maxbrenner-ai

# Possible inform and request slots for the agent
agent_inform_slots = ['moviename', 'theater', 'starttime', 'date', 'genre', 'state', 'city', 'zip', 'critic_rating',
                     'mpaa_rating', 'distanceconstraints', 'video_format', 'theater_chain', 'price', 'actor',
                     'description', 'other', 'numberofkids']
agent_request_slots = ['moviename', 'theater', 'starttime', 'date', 'numberofpeople', 'genre', 'state', 'city', 'zip',
                      'critic_rating', 'mpaa_rating', 'distanceconstraints', 'video_format', 'theater_chain', 'price',
                      'actor', 'description', 'other', 'numberofkids']

# Possible actions for agent
agent_actions = [

maxbrenner-ai

def _build_model(self):
   model = Sequential()
   model.add(Dense(self.hidden_size, input_dim=self.state_size, activation='relu'))
   model.add(Dense(self.num_actions, activation='linear'))
   model.compile(loss='mse', optimizer=Adam(lr=self.lr))
   return model

maxbrenner-ai

def get_action(self, state, use_rule=False):
   # self.eps is initialized to the starting epsilon and does NOT get annealed
   if self.eps > random.random():
       index = random.randint(0, self.num_actions - 1)
       # self._map_index_to_action(index) takes an index and maps the action from all possible agent actions
       action = self._map_index_to_action(index)
       return index, action
   else:
       if use_rule:
           return self._rule_action()

maxbrenner-ai

def _rule_action(self):
   # self.rule_current_slot_index points to current slot
   # rule_requests defined in dialogue_config.py
   if self.rule_current_slot_index < len(rule_requests):
       slot = rule_requests[self.rule_current_slot_index]
       self.rule_current_slot_index += 1
       rule_response = {'intent': 'request', 'inform_slots': {}, 'request_slots': {slot: 'UNK'}}
   # self.rule_phase used to indicate if we are at second to last round or last round
   elif self.rule_phase == 'not done':
       rule_response = {'intent': 'match_found', 'inform_slots': {}, 'request_slots': {}}

maxbrenner-ai

def reset(self):
   self.rule_current_slot_index = 0
   self.rule_phase = 'not done'

maxbrenner-ai

def _dqn_action(self, state):
   # self.beh_model is our keras behavior model
   index = np.argmax(self.beh_model.predict(state.reshape(1, self.state_size), target=target).flatten())
   action = self._map_index_to_action(index)
   return index, action