def __init__(self, enemies):
self.enemies = enemies
experiment_num = 0
while True:
self.experiment_name = 'task2_generalist3_enemies_{}_{}'.format(
enemies, experiment_num)
if not os.path.exists(self.experiment_name):
break
experiment_num += 1
os.makedirs(self.experiment_name)
self.env = Environment(
experiment_name=self.experiment_name,
level=2,
player_controller=player_controller(N_HIDDEN_NEURONS),
enemies=[enemies[0]],
speed="fastest")
self.n_vars = (self.env.get_num_sensors() +
1) * N_HIDDEN_NEURONS + (N_HIDDEN_NEURONS + 1) * 5
self.rot_size = int((self.n_vars * (self.n_vars - 1)) / 2)
self.dev = np.random.uniform(0, INIT_SD, (NPOP, self.n_vars))
self.rot = np.random.uniform(-np.pi, np.pi, (NPOP, self.rot_size))
self.saw = np.ones(np.shape(enemies))
self.init = Initialization(DOM_L, DOM_U)
self.evaluator = Evaluation(self.env, enemies, SHARE_SIZE)
self.selector = Selection()
self.logger = Logger(self.experiment_name)
self.recombinator = Recombination()
self.mutator = Mutation(MIN_DEV, ROTATION_MUTATION, STANDARD_DEVIATION,
DOM_L, DOM_U)
class Generalist2:
def __init__(self, enemies):
self.enemies = enemies
self.experiment_name = 'task2_generalist2_enemies_{}'.format(enemies)
if not os.path.exists(self.experiment_name):
os.makedirs(self.experiment_name)
self.env = Environment(experiment_name=self.experiment_name, level=2,
player_controller=player_controller(N_HIDDEN_NEURONS),
enemies=[enemies[0]],
speed="fastest")
self.n_vars = (self.env.get_num_sensors() + 1) * N_HIDDEN_NEURONS + (N_HIDDEN_NEURONS + 1) * 5
self.rot_size = int((self.n_vars * (self.n_vars - 1)) / 2)
self.dev = np.random.uniform(0, INIT_SD, (NPOP, self.n_vars))
self.rot = np.random.uniform(-np.pi, np.pi, (NPOP, self.rot_size))
self.init = Initialization(DOM_L, DOM_U)
self.evaluator = Evaluation(self.env, enemies, SHARE_SIZE)
self.selector = Selection()
self.logger = Logger(self.experiment_name)
self.recombinator = Recombination()
self.mutator = Mutation(MIN_DEV, ROTATION_MUTATION, STANDARD_DEVIATION, DOM_L, DOM_U)
def __compare_to_ultimate__(self, individual_gain, wins, champion):
ultimate_performance_file = open("Logs/Task1/UltimateChampion/UltimatePerformance.txt", "r+")
ultimate_performance, ultimate_wins = eval(ultimate_performance_file.read())
#declare new ultimate champion if either has more wins or same amount of wins and greater performance
if wins >= ultimate_wins and ((wins > ultimate_wins) or (individual_gain > ultimate_performance)):
ultimate_file = open("Logs/Task1/UltimateChampion/UltimateChampion.txt", "w")
ultimate_file.write(np.array_str(champion))
ultimate_performance_file.seek(0)
ultimate_performance_file.truncate()
ultimate_performance_file.write("".join(map(str, (individual_gain,", ", wins))))
def __run_best_against_all__(self):
player_array, enemy_array = [], []
wins = 0
for i in range(1, 9):
self.env.update_parameter('enemies', [i])
_, player_life, enemy_life, _ = self.env.play(pcont=np.array(self.best_individual[0]))
player_array.append(player_life)
enemy_array.append(enemy_life)
if enemy_life == 0:
wins += 1
return (sum(player_array) - sum(enemy_array)), wins
def __share_of__(self, ind1, ind2):
dist = np.linalg.norm(ind1 - ind2)
if dist > SHARE_SIZE:
return 0
return 1 - dist / SHARE_SIZE
def __share_fitness__(self, pop, fitness):
new_fitness = []
length = len(pop)
for i in range(length):
divisor = sum([self.__share_of__(pop[i], pop[j]) for j in range(length)])
new_fitness.append(fitness[i] / divisor)
return np.array(new_fitness)
def store_best_champion(self, pop, fit, gen):
if fit.max() > self.highest_fitness:
self.best_individual = self.selector.select_best_n(pop,fit,1)
self.best_gen = gen
self.highest_fitness = fit.max()
def run(self):
population = self.init.uniform_initialization(NPOP, self.n_vars)
self.best_gen = 0
self.highest_fitness = -100000
self.best_individual = None
for generation in range(1,NGEN+1):
print("EVALUATION GENERATION %d OF %d \n" %(generation, NGEN))
fitness_list = self.evaluator.sharing_generalist_eval(population)
'''Log fitness'''
self.logger.log_results(fitness_list, population)
self.store_best_champion(population, fitness_list, generation)
min_fitness = np.amin(fitness_list)
if min_fitness < 0:
fitness_list = [x - min_fitness for x in fitness_list]
fitness_list = self.__share_fitness__(population, fitness_list)
'''create next gen'''
if generation != NGEN:
parents = self.selector.tournament_percentage(population, fitness_list)
survivors = self.selector.select_best_percentage(population, fitness_list, BEST_SURVIVOR_PERCENTAGE)
'''create children'''
children = self.recombinator.blend(parents, NPOP-len(survivors))
#children, self.dev, self.rot = self.mutator.correlated_mutation(children, self.dev, self.rot)
children, self.dev = self.mutator.uncorrelated_mutation_n_step_size(children, self.dev)
'''combine survivors and children'''
population = np.append(children, survivors, axis=0)
# Run the best individual of all generations
print("The best fitness was in generation %d and had a fitness of %.3f" %(self.best_gen, self.highest_fitness))
total_individual_gain = 0
total_wins = 0
for i in range(5): #!change back to 5
individual_gain, wins = self.__run_best_against_all__()
total_individual_gain += individual_gain
total_wins += wins
average_ig = total_individual_gain/5
average_wins = total_wins/5
self.__compare_to_ultimate__(average_ig, average_wins, self.best_individual[0])
self.logger.log_individual(average_ig)
os.makedirs(experiment_name)
env = Environment(experiment_name=experiment_name,
level=2,
player_controller=player_controller(N_HIDDEN_NEURONS),
enemies=ENEMY,
speed="fastest")
n_vars = (env.get_num_sensors() + 1) * N_HIDDEN_NEURONS + (N_HIDDEN_NEURONS +
1) * 5
rot_size = int((n_vars * (n_vars - 1)) / 2)
dev = np.random.uniform(0, INIT_SD, (NPOP, n_vars))
rot = np.random.uniform(-np.pi, np.pi, (NPOP, rot_size))
init = Initialization(DOM_L, DOM_U)
evaluator = Evaluation(env)
selector = Selection()
logger = Logger(experiment_name)
recombinator = Recombination()
mutator = Mutation(MIN_DEV, ROTATION_MUTATION, STANDARD_DEVIATION, DOM_L,
DOM_U)
'''
Changes with regards to specialist1:
* Specialist 2 uses tournament selection (instead of selecting the best)
* Specialist 2 uses blend recombination (instead of simple)
* Specialist 2 uses correlated mutation (instead of nonuniform)
'''
class Generalist3:
def __init__(self, enemies):
self.enemies = enemies
experiment_num = 0
while True:
self.experiment_name = 'task2_generalist3_enemies_{}_{}'.format(
enemies, experiment_num)
if not os.path.exists(self.experiment_name):
break
experiment_num += 1
os.makedirs(self.experiment_name)
self.env = Environment(
experiment_name=self.experiment_name,
level=2,
player_controller=player_controller(N_HIDDEN_NEURONS),
enemies=[enemies[0]],
speed="fastest")
self.n_vars = (self.env.get_num_sensors() +
1) * N_HIDDEN_NEURONS + (N_HIDDEN_NEURONS + 1) * 5
self.rot_size = int((self.n_vars * (self.n_vars - 1)) / 2)
self.dev = np.random.uniform(0, INIT_SD, (NPOP, self.n_vars))
self.rot = np.random.uniform(-np.pi, np.pi, (NPOP, self.rot_size))
self.saw = np.ones(np.shape(enemies))
self.init = Initialization(DOM_L, DOM_U)
self.evaluator = Evaluation(self.env, enemies, SHARE_SIZE)
self.selector = Selection()
self.logger = Logger(self.experiment_name)
self.recombinator = Recombination()
self.mutator = Mutation(MIN_DEV, ROTATION_MUTATION, STANDARD_DEVIATION,
DOM_L, DOM_U)
def __compare_to_ultimate__(self, individual_gain, wins, champion):
ultimate_performance_file = open(
"Logs/Task1/UltimateChampion/UltimatePerformance.txt", "r+")
ultimate_performance, ultimate_wins = eval(
ultimate_performance_file.read())
#declare new ultimate champion if either has more wins or same amount of wins and greater performance
if wins >= ultimate_wins and (
(wins > ultimate_wins) or
(individual_gain > ultimate_performance)):
ultimate_file = open(
"Logs/Task1/UltimateChampion/UltimateChampion.txt", "w")
ultimate_file.write(np.array_str(champion))
ultimate_performance_file.seek(0)
ultimate_performance_file.truncate()
ultimate_performance_file.write("".join(
map(str, (individual_gain, ", ", wins))))
def __run_best_against_all__(self):
player_array, enemy_array = [], []
wins = 0
for i in range(1, 9):
self.env.update_parameter('enemies', [i])
_, player_life, enemy_life, _ = self.env.play(
pcont=np.array(self.best_individual[0]))
player_array.append(player_life)
enemy_array.append(enemy_life)
if enemy_life == 0:
wins += 1
return (sum(player_array) - sum(enemy_array)), wins
def __stepwise_adaption_of_weights__(self, saw):
fitness_array = np.zeros(8)
for i in range(1, 9):
self.env.update_parameter('enemies', [i])
fitness, _, _, _ = self.env.play(
pcont=np.array(self.best_individual[0]))
fitness_array[i - 1] = fitness
fitness_indices = np.argsort(fitness_array)
max_index = 7
min_index = 0
while max_index != min_index:
if saw[fitness_indices[max_index]] <= 0.11:
max_index -= 1
elif saw[fitness_indices[min_index]] >= 1.89:
min_index += 1
else:
saw[fitness_indices[max_index]] -= 0.1
saw[fitness_indices[min_index]] += 0.1
break
return saw
def __share_of__(self, ind1, ind2):
dist = np.linalg.norm(ind1 - ind2)
if dist > SHARE_SIZE:
return 0
return 1 - dist / SHARE_SIZE
def __share_fitness__(self, pop, fitness):
new_fitness = []
length = len(pop)
for i in range(length):
divisor = sum(
[self.__share_of__(pop[i], pop[j]) for j in range(length)])
new_fitness.append(fitness[i] / divisor)
return np.array(new_fitness)
def store_best_champion(self, pop, fit, gen):
if fit.max() > self.highest_fitness:
self.best_individual = self.selector.select_best_n(pop, fit, 1)
self.best_gen = gen
self.highest_fitness = fit.max()
def run(self):
population = self.init.uniform_initialization(NPOP, self.n_vars)
self.best_gen = 0
self.highest_fitness = -100000
self.best_individual = None
for generation in itertools.count(start=1):
print("\nEVALUATION GENERATION %d \n" % generation)
fitness_list = self.evaluator.sharing_generalist_eval(population,
saw=self.saw)
'''Log fitness'''
self.logger.log_results(fitness_list, population)
self.store_best_champion(population, fitness_list, generation)
min_fitness = np.amin(fitness_list)
print("Fitness before normalization:\n" + str(fitness_list))
if min_fitness < 0:
fitness_list = [x - min_fitness for x in fitness_list]
print("Fitness after normalization:\n" + str(fitness_list))
fitness_list = self.__share_fitness__(population, fitness_list)
print("Fitness after sharing:\n" + str(fitness_list))
'''recalculate SAW array'''
self.saw = self.__stepwise_adaption_of_weights__(self.saw)
print("\nSAW: ", self.saw, "\n")
'''every 10 generations check champion against ultimate'''
if generation % 10 == 0:
print(
"The best fitness was in generation %d and had a fitness of %.3f"
% (self.best_gen, self.highest_fitness))
total_individual_gain = 0
total_wins = 0
for i in range(5):
print("CHAMPION OF GENERATION %d, RUN %d OF %d \n" %
(generation, (i + 1), 5))
individual_gain, wins = self.__run_best_against_all__()
total_individual_gain += individual_gain
total_wins += wins
average_ig = total_individual_gain / 5
average_wins = total_wins / 5
print("CHAMPION OF GENERATION %d, IG: %d\n" %
(generation, average_ig))
self.__compare_to_ultimate__(average_ig, average_wins,
self.best_individual[0])
self.logger.log_individual(average_ig)
'''create next gen'''
parents = self.selector.tournament_percentage(
population, fitness_list)
survivors = self.selector.select_best_percentage(
population, fitness_list, BEST_SURVIVOR_PERCENTAGE)
'''create children'''
children = self.recombinator.blend(parents, NPOP - len(survivors))
#children, self.dev, self.rot = self.mutator.correlated_mutation(children, self.dev, self.rot)
children, self.dev = self.mutator.uncorrelated_mutation_n_step_size(
children, self.dev)
'''combine survivors and children'''
population = np.append(children, survivors, axis=0)