def take_control(self):
""" Always answers yes """
obs = yield
action = rng.choice([Yes, No])
response = yield action
p_valid = self._valid_detector.action_valid(action, first_sentence(response))
success = (p_valid > 0.5)
self.record(success)
self._eagerness = 0.
def get_double_action(self):
if len(kg.player_location.entities) + len(kg.inventory.entities) <= 1:
return None
entity1 = None
entity2 = None
count = 0
while id(entity1) == id(entity2):
if count == 100:
return None # Failsafe
else:
count += 1
entity1 = self.get_random_entity()
entity2 = self.get_random_entity()
verb, prep = rng.choice(complex_verbs)
return DoubleAction(verb, entity1, prep, entity2)
def extract_single_object_actions(self, entity):
if id(entity) in self.cached_extractions:
return self.cached_extractions[id(entity)]
# Collect known actions for the given entity.
noun = entity.name
actions_to_try = []
unknown_actions_to_exclude = {}
for affordable_attribute in self.affordable_attributes:
est_prob = self.estimate_attribute_prob(
noun, affordable_attribute.attribute_name)
if est_prob >= affordable_attribute.known_action_extraction_threshold:
for known_action in affordable_attribute.known_actions_to_try:
actions_to_try.append((known_action(entity), est_prob))
for unknown_action in affordable_attribute.unknown_actions_to_exclude:
unknown_actions_to_exclude[unknown_action] = True
# Collect unknown actions for the given entity.
unknown_actions_to_try = self.extract_unknown_actions_with_log_probs(
noun)
for a_lp in unknown_actions_to_try:
if a_lp[0] not in self.action_prior_table:
self.action_prior_table[a_lp[0]] = -1. # Needs human review.
for a_lp in unknown_actions_to_try:
prob = self.estimate_unknown_action_prob(a_lp[1])
prob *= max(0., self.action_prior_table[
a_lp[0]]) # Treat unreviewed actions (-1) as 0.0
if prob > self.unknown_action_extraction_threshold:
action_text = a_lp[0]
action_minus_the = action_text[:-4]
if action_minus_the not in unknown_actions_to_exclude.keys():
if action_minus_the in self.unknown_actions_to_promote:
target_action = rng.choice(
self.unknown_actions_to_promote[action_minus_the])
action_object = target_action(entity)
else:
action_object = action.SingleAction(
action_text, entity)
actions_to_try.append((action_object, prob))
# Sort by descending probability of expected value of taking the action.
actions_to_try.sort(key=lambda tup: tup[1], reverse=True)
self.cached_extractions[id(entity)] = actions_to_try
return actions_to_try
def get_single_object_action(self):
entity = self.get_random_entity()
if not entity:
return None
verb = rng.choice(single_object_verbs)
return SingleAction(verb, entity)
def get_standalone_action(self):
return StandaloneAction(rng.choice(standalone_verbs))
def get_random_entity(self):
""" Returns a random entity from the location or inventory. """
if kg.player_location.entities or kg.inventory.entities:
return rng.choice(kg.player_location.entities +
kg.inventory.entities)
return None