async def run():
"""
The main coroutine, which is started below.
"""
n_cores = 1
experiment_conf = ExperimentConfig()
settings = parser.parse_args()
queue = SimulatorQueue(n_cores, settings)
await queue.start()
population = Population(experiment_conf.population_conf)
await population.init_pop(queue)
# population.simulator_connection.disconnect()
# await simulator_supervisor.relaunch()
gen_num = 0
while gen_num < experiment_conf.num_generations:
population = await population.next_gen(gen_num + 1, queue)
# simulator_connection.disconnect()
# simulator_supervisor.relaunch()
# population.simulator_connection = await World.create(settings)
gen_num += 1
async def run():
"""
The main coroutine, which is started below.
"""
# experiment params #
num_generations = 100
population_size = 100
offspring_size = 50
genotype_conf = PlasticodingConfig(
max_structural_modules=100,
)
mutation_conf = MutationConfig(
mutation_prob=0.8,
genotype_conf=genotype_conf,
)
crossover_conf = CrossoverConfig(
crossover_prob=0.8,
)
# experiment params #
# Parse command line / file input arguments
settings = parser.parse_args()
experiment_management = ExperimentManagement(settings)
do_recovery = settings.recovery_enabled and not experiment_management.experiment_is_new()
logger.info('Activated run '+settings.run+' of experiment '+settings.experiment_name)
if do_recovery:
gen_num, has_offspring, next_robot_id = experiment_management.read_recovery_state(population_size, offspring_size)
if gen_num == num_generations-1:
logger.info('Experiment is already complete.')
return
else:
gen_num = 0
next_robot_id = 1
def fitness_function(robot_manager, robot):
contacts = measures.contacts(robot_manager, robot)
assert(contacts != 0)
return fitness.displacement_velocity_hill(robot_manager, robot)
population_conf = PopulationConfig(
population_size=population_size,
genotype_constructor=random_initialization,
genotype_conf=genotype_conf,
fitness_function=fitness_function,
mutation_operator=standard_mutation,
mutation_conf=mutation_conf,
crossover_operator=standard_crossover,
crossover_conf=crossover_conf,
selection=lambda individuals: tournament_selection(individuals, 2),
parent_selection=lambda individuals: multiple_selection(individuals, 2, tournament_selection),
population_management=steady_state_population_management,
population_management_selector=tournament_selection,
evaluation_time=settings.evaluation_time,
offspring_size=offspring_size,
experiment_name=settings.experiment_name,
experiment_management=experiment_management,
)
settings = parser.parse_args()
simulator_queue = SimulatorQueue(settings.n_cores, settings, settings.port_start)
await simulator_queue.start()
population = Population(population_conf, simulator_queue, next_robot_id)
if do_recovery:
# loading a previous state of the experiment
await population.load_snapshot(gen_num)
if gen_num >= 0:
logger.info('Recovered snapshot '+str(gen_num)+', pop with ' + str(len(population.individuals))+' individuals')
if has_offspring:
individuals = await population.load_offspring(gen_num, population_size, offspring_size, next_robot_id)
gen_num += 1
logger.info('Recovered unfinished offspring '+str(gen_num))
if gen_num == 0:
await population.init_pop(individuals)
else:
population = await population.next_gen(gen_num, individuals)
experiment_management.export_snapshots(population.individuals, gen_num)
else:
# starting a new experiment
experiment_management.create_exp_folders()
await population.init_pop()
experiment_management.export_snapshots(population.individuals, gen_num)
while gen_num < num_generations-1:
gen_num += 1
population = await population.next_gen(gen_num)
experiment_management.export_snapshots(population.individuals, gen_num)
async def run():
"""
The main coroutine, which is started below.
"""
# Parse command line / file input arguments
genotype_conf = PlasticodingConfig(max_structural_modules=100, )
mutation_conf = MutationConfig(
mutation_prob=0.8,
genotype_conf=genotype_conf,
)
crossover_conf = CrossoverConfig(crossover_prob=0.8, )
settings = parser.parse_args()
experiment_management = ExperimentManagement(settings)
population_conf = PopulationConfig(
population_size=100,
genotype_constructor=random_initialization,
genotype_conf=genotype_conf,
fitness_function=fitness.displacement_velocity_hill,
mutation_operator=standard_mutation,
mutation_conf=mutation_conf,
crossover_operator=standard_crossover,
crossover_conf=crossover_conf,
selection=lambda individuals: tournament_selection(individuals, 2),
parent_selection=lambda individuals: multiple_selection(
individuals, 2, tournament_selection),
population_management=steady_state_population_management,
population_management_selector=tournament_selection,
evaluation_time=settings.evaluation_time,
offspring_size=50,
experiment_name=settings.experiment_name,
experiment_management=experiment_management,
)
settings = parser.parse_args()
simulator_queue = SimulatorQueue(settings.n_cores, settings,
settings.port_start)
await simulator_queue.start()
population = Population(population_conf, simulator_queue, 0)
# choose a snapshot here. and the maximum best individuals you wish to watch
generation = 100
max_best = 10
await population.load_snapshot(generation)
fitnesses = []
for ind in population.individuals:
fitnesses.append(ind.fitness)
fitnesses = np.array(fitnesses)
ini = len(population.individuals) - max_best
fin = len(population.individuals)
population.individuals = np.array(population.individuals)
population.individuals = population.individuals[np.argsort(fitnesses)
[ini:fin]]
await population.evaluate(population.individuals, generation)
async def run():
"""
The main coroutine, which is started below.
"""
# experiment params #
num_generations = 200
population_size = 100
offspring_size = 100
front = 'slaves'
# environment world and z-start
environments = {'plane': 0.03,
'tilted5': 0.1
}
genotype_conf = PlasticodingConfig(
max_structural_modules=15,
plastic=True,
)
mutation_conf = MutationConfig(
mutation_prob=0.8,
genotype_conf=genotype_conf,
)
crossover_conf = CrossoverConfig(
crossover_prob=0.8,
)
# experiment params #
# Parse command line / file input arguments
settings = parser.parse_args()
experiment_management = ExperimentManagement(settings, environments)
do_recovery = settings.recovery_enabled and not experiment_management.experiment_is_new()
logger.info('Activated run '+settings.run+' of experiment '+settings.experiment_name)
if do_recovery:
gen_num, has_offspring, next_robot_id = experiment_management.read_recovery_state(population_size,
offspring_size)
if gen_num == num_generations-1:
logger.info('Experiment is already complete.')
return
else:
gen_num = 0
next_robot_id = 1
def fitness_function_plane(robot_manager, robot):
return fitness.displacement_velocity_hill(robot_manager, robot, False)
fitness_function = {'plane': fitness_function_plane,
'tilted5': fitness_function_plane}
population_conf = PopulationConfig(
population_size=population_size,
genotype_constructor=random_initialization,
genotype_conf=genotype_conf,
fitness_function=fitness_function,
mutation_operator=standard_mutation,
mutation_conf=mutation_conf,
crossover_operator=standard_crossover,
crossover_conf=crossover_conf,
selection=lambda individuals: tournament_selection(individuals, environments, 2),
parent_selection=lambda individuals: multiple_selection(individuals, 2, tournament_selection, environments),
population_management=steady_state_population_management,
population_management_selector=tournament_selection,
evaluation_time=settings.evaluation_time,
offspring_size=offspring_size,
experiment_name=settings.experiment_name,
experiment_management=experiment_management,
environments=environments,
front=front
)
settings = parser.parse_args()
simulator_queue = {}
analyzer_queue = None
previous_port = None
for environment in environments:
settings.world = environment
settings.z_start = environments[environment]
if previous_port is None:
port = settings.port_start
previous_port = port
else:
port = previous_port+settings.n_cores
previous_port = port
simulator_queue[environment] = SimulatorQueue(settings.n_cores, settings, port)
await simulator_queue[environment].start()
analyzer_queue = AnalyzerQueue(1, settings, port+settings.n_cores)
await analyzer_queue.start()
population = Population(population_conf, simulator_queue, analyzer_queue, next_robot_id)
if do_recovery:
if gen_num >= 0:
# loading a previous state of the experiment
await population.load_snapshot(gen_num)
logger.info('Recovered snapshot '+str(gen_num)+', pop with ' + str(len(population.individuals))+' individuals')
if has_offspring:
individuals = await population.load_offspring(gen_num, population_size, offspring_size, next_robot_id)
gen_num += 1
logger.info('Recovered unfinished offspring '+str(gen_num))
if gen_num == 0:
await population.init_pop(individuals)
else:
population = await population.next_gen(gen_num, individuals)
experiment_management.export_snapshots(population.individuals, gen_num)
else:
# starting a new experiment
experiment_management.create_exp_folders()
await population.init_pop()
experiment_management.export_snapshots(population.individuals, gen_num)
while gen_num < num_generations-1:
gen_num += 1
population = await population.next_gen(gen_num)
experiment_management.export_snapshots(population.individuals, gen_num)
async def run():
"""
The main coroutine, which is started below.
"""
# experiment params #
num_generations = 100
population_size = 100
offspring_size = 50
front = 'slaves'
# environment world and z-start
environments = {'plane': 0.03, 'tilted5': 0.1}
genotype_conf = PlasticodingConfig(
max_structural_modules=15,
plastic=True,
)
mutation_conf = MutationConfig(
mutation_prob=0.8,
genotype_conf=genotype_conf,
)
crossover_conf = CrossoverConfig(crossover_prob=0.8, )
# experiment params #load_individual
# Parse command line / file input arguments
settings = parser.parse_args()
experiment_management = ExperimentManagement(settings, environments)
do_recovery = settings.recovery_enabled and not experiment_management.experiment_is_new(
)
logger.info('Activated run ' + settings.run + ' of experiment ' +
settings.experiment_name)
def fitness_function(robot_manager, robot):
#contacts = measures.contacts(robot_manager, robot)
#assert(contacts != 0)
return fitness.displacement_velocity_hill(robot_manager, robot, False)
population_conf = PopulationConfig(
population_size=population_size,
genotype_constructor=random_initialization,
genotype_conf=genotype_conf,
fitness_function=fitness_function,
mutation_operator=standard_mutation,
mutation_conf=mutation_conf,
crossover_operator=standard_crossover,
crossover_conf=crossover_conf,
selection=lambda individuals: tournament_selection(
individuals, environments, 2),
parent_selection=lambda individuals: multiple_selection(
individuals, 2, tournament_selection, environments),
population_management=steady_state_population_management,
population_management_selector=tournament_selection,
evaluation_time=settings.evaluation_time,
offspring_size=offspring_size,
experiment_name=settings.experiment_name,
experiment_management=experiment_management,
environments=environments,
front=front)
settings = parser.parse_args()
simulator_queue = {}
analyzer_queue = None
previous_port = None
for environment in environments:
settings.world = environment
settings.z_start = environments[environment]
if previous_port is None:
port = settings.port_start
previous_port = port
else:
port = previous_port + settings.n_cores
previous_port = port
simulator_queue[environment] = SimulatorQueue(settings.n_cores,
settings, port)
await simulator_queue[environment].start()
analyzer_queue = AnalyzerQueue(1, settings, port + settings.n_cores)
await analyzer_queue.start()
population = Population(population_conf, simulator_queue, analyzer_queue,
1)
# choose a snapshot here. and the maximum best individuals you wish to watch
generation = 61 #99
max_best = 3 #10
await population.load_snapshot(generation)
values = []
for ind in population.individuals:
# define a criteria here
for environment in environments:
ind[environment].evaluated = False
values.append(ind[list(environments.keys())[-1]].consolidated_fitness)
#values.append(ind['plane'].phenotype._behavioural_measurements.displacement_velocity_hill)
values = np.array(values)
ini = len(population.individuals) - max_best
fin = len(population.individuals)
population.individuals = np.array(population.individuals)
# highest
population.individuals = population.individuals[np.argsort(values)
[ini:fin]]
# lowest
#population.individuals = population.individuals[np.argsort(values)[0:max_best]]
for ind in population.individuals:
print(ind[list(environments.keys())[-1]].phenotype.id)
print('consolidated_fitness',
ind[list(environments.keys())[-1]].consolidated_fitness)
for environment in environments:
print(environment)
await population.evaluate(new_individuals=population.individuals,
gen_num=generation,
environment=environment,
type_simulation='watch')