def __init__(self):
self.flappybird = FlappyBird()
self.current_observation = None
self.current_theory = None
self.action_counter = 0
self.theories_manager = TheoriesManager()
self.theories_manager.get_theories_from_json('theories_saved.json')
self.just_restarted = False
self.turns_for_jump = 0
class Agent:
def __init__(self):
self.flappybird = FlappyBird()
def init(self):
pass
"""
* Method used to determine the next move to be performed by the agent.
* now is moving random
"""
def act(self):
self.observeworld()
if self.myRandom() == 2:
self.flappybird.holdKeyDown()
else:
self.flappybird.releaseKey()
def myRandom(self):
return random.randint(0, 2)
def observeworld(self):
positions = self.flappybird.getWorldPositionObjets()
print("Upper block: ", positions[0])
print("Bottom block: ", positions[1])
print("Bird: ", positions[0])
print("Count: ", self.flappybird.counter)
print("Dead: ", self.flappybird.dead)
def run(self):
self.flappybird.initGame()
while True:
self.flappybird.eachCicle()
self.act()
def main(): best = None best_speed = None tmp_speed = None fp = FlappyBird() bird = Bird() best_speed = fp.run(bird, best, best_speed) best = bird while True: bird = Bird() tmp_speed = fp.run(bird, best, best_speed) if bird.fitness > best.fitness: best = bird best_speed = tmp_speed
def main():
n_birds = 15
n_generations = 1000
best_info = None
fp = FlappyBird()
ga = GeneticAlgorithm(n_birds, lambda brain: Bird(_brain=brain))
birds = ga.init_population()
fp.run(birds, best_info)
birds, best_info = ga.evolve(birds)
best_info = best_info + (1, )
show_best(best_info)
for i in xrange(n_generations):
fp.run(birds, best_info)
birds, tmp = ga.evolve(birds)
if tmp[2] > best_info[2]:
best_info = tmp + (i + 2, )
show_best(best_info)
def flappy_game(self):
game = FlappyBird()
game.run()
class Agent:
def __init__(self):
self.flappybird = FlappyBird()
self.current_observation = None
self.current_theory = None
self.action_counter = 0
self.theories_manager = TheoriesManager()
self.theories_manager.get_theories_from_json('theories_saved.json')
self.just_restarted = False
self.turns_for_jump = 0
def init(self):
pass
"""
* Method used to determine the next move to be performed by the agent.
* now is moving random
"""
def print_relevant(self):
print('-----------------')
print("Cycles: ", self.action_counter)
print("Current Observation: ", self.current_observation.get_code())
print("Dead: ", self.flappybird.dead)
print("Theory count: ", self.theories_manager.theories_size()
) #Not really theory count, actually scenarios theorized about
def act(self):
if self.turns_for_jump > 0:
self.turns_for_jump -= 1
self.bird_act(False)
return
self.observe_world()
if self.current_theory is not None:
self.update_theory()
self.print_relevant()
if self.current_observation.get_dead_state():
self.current_theory = None
self.bird_act(False)
else:
if self.action_counter > 1 and self.action_counter % 10000 == 0:
self.theories_manager.save_theories_to_json(
'theories_saved.json')
jump = self.choose_action()
self.action_counter += 1
self.turns_for_jump = 4
self.bird_act(jump)
def choose_action(self):
if self.action_counter < 2000:
self.act_from_theories_with_exploration(19)
elif self.action_counter < 5000:
self.act_from_theories_with_exploration(10)
elif self.action_counter < 10000:
self.act_from_theories_with_exploration(5)
else:
self.act_from_theories_with_exploration(2)
return self.current_theory.get_jump()
def update_theory(self):
theory_is_finished = self.current_theory.is_finished()
theory_was_correct = self.current_theory.is_correct(
self.current_observation)
if theory_is_finished and theory_was_correct:
self.theories_manager.update_theory(self.current_theory)
elif theory_is_finished:
new_theory = self.theories_manager.new_theory(
self.current_theory.get_observation_before(),
self.current_theory.get_jump())
self.theories_manager.finish_and_add_theory(
new_theory, self.current_observation, self.just_restarted)
else:
self.theories_manager.finish_and_add_theory(
self.current_theory, self.current_observation,
self.just_restarted)
def my_random(self):
return random.randint(0, 20)
def observe_world(self):
positions = self.flappybird.getWorldPositionObjets()
self.current_observation = Observation(self.flappybird.counter,
self.flappybird.dead)
self.current_observation.set_relative_positions(positions)
self.just_restarted = self.current_observation.just_restarted(
positions)
def bird_act(self, jump):
if jump:
self.turns_for_jump = 9
self.flappybird.holdKeyDown()
else:
self.flappybird.releaseKey()
def run(self):
self.flappybird.initGame()
while True:
self.flappybird.eachCicle()
self.act()
def act_from_theories_with_exploration(self, probability):
should_not_explore = self.my_random() > probability
best_theory, both_actions_already_explored, death_actions = self.theories_manager.get_best_theory(
self.current_observation)
if best_theory is not None:
theory_may_cause_death = death_actions[int(best_theory.get_jump())]
if death_actions[0] and death_actions[1]:
# theory_may_cause_death = False
print('DEATH FOR BOTH ACTIONS')
elif theory_may_cause_death:
print('POSSIBLE DEATH!! IF ', best_theory.get_jump())
if both_actions_already_explored or should_not_explore:
self.current_theory = best_theory
print('THEORY BASED: ', self.next_action())
else:
opposite_action = not best_theory.get_jump()
self.current_theory = self.theories_manager.new_theory(
self.current_observation, opposite_action)
print('EXPLORING: ', self.next_action())
else:
self.random_act()
def random_act(self):
jump = self.my_random() == 2
self.current_theory = self.theories_manager.new_theory(
self.current_observation, jump)
print('ACTING RANDOM: ', self.next_action())
def next_action(self):
if self.current_theory.get_jump():
return 'JUMP'
else:
return 'FALL'
def __init__(self):
self.flappybird = FlappyBird();