def __init__(self, path, reset_cost, default_reward, value_expert):
self.world = Model(path, reset_cost, default_reward)
self.value_expert = value_expert
in_letters = [Letter(symbol) for symbol in self.world.mrm.observations]
self.kbase = MRMActiveKnowledgeBase(self.world)
self.OT = ObservationTable(input_letters=in_letters,
knowledge_base=self.kbase)
print('Initializing OT')
self.OT.initialize()
print('OT initialized')
#COUNTERS
self.total_learning_time = 0
self.total_exploring_time = 0
self.rewards = 0
self.iteration4Explor = 0
self.iteration4OT = 0
self.nuof_counter_examples = 0
#EXECUTION
self.learn()
while not self.check():
self.learn()
#END PRINT
self.endPrints()
remove(TMP_MODEL_PATH)
def __init__(self, input_vocabulary, knowledge_base, max_states, tmp_dir=None, eqtests=None):
"""Implementation of the LSTAR algorithm.
Per default, WPMethod is used for equivalence tests. However, one can prefer a RandomWalkMethod
by specifying the following 'eqtests' parameter:
eqtests = RandomWalkMethod(self.knowledge_base, self.input_letters, 10000, 0.7)
"""
self.input_letters = [Letter(symbol) for symbol in input_vocabulary]
self.knowledge_base = knowledge_base
self.tmp_dir = tmp_dir
self.observation_table = ObservationTable(self.input_letters, self.knowledge_base)
self.max_states = max_states
self.eqtests = eqtests
self.__f_stop = False
