def _Test_Draw():
T = 1000
quad = Quadruped()
sim = PYROSIM(playPaused=False, playBlind=True, evalTime=T)
quad.Send_To_Simulator(sim)
sim.Start()
sim.Wait_To_Finish()
data = sim.Get_Results()
print data[4, 1, T - 1]
sim2 = PYROSIM(playPaused=False, playBlind=False, evalTime=T)
quad.Send_To_Simulator(sim2)
sim2.Start()
sim2.Wait_To_Finish()
data = sim2.Get_Results()
print data[4, 1, T - 1]
mutant = quad.Copy_And_Mutate(variable=.99, sigma=5)
sim3 = PYROSIM(playPaused=False, playBlind=False, evalTime=T)
IDs = quad.Send_To_Simulator(sim3)
print IDs
IDs = mutant.Send_To_Simulator(sim3,
objID=IDs[0],
jointID=IDs[1],
sensorID=IDs[2],
neuronID=IDs[3])
sim3.Start()
sim3.Wait_To_Finish()
data = sim3.Get_Results()
print data[4, 1, T - 1], data[9, 1, T - 1]
def _Test_Tree():
T = 1000
sim = PYROSIM(playPaused=False,
playBlind=False,
evalTime=T,
xyz=[0, 0, 5],
hpr=[90, -90, 0])
#sim = PYROSIM(playPaused=False, playBlind=False, evalTime=T, xyz=[-5,0,2], hpr=[0,-20,0])
t = Treebot()
t.Send_To_Simulator(sim)
sim.Start()
sim.Wait_To_Finish()
data = sim.Get_Results()
def Play_Indv(robot, eval_time, environment=False):
sensor_data = {}
if environment:
for i, env in enumerate(environment[:8]):
sim = PYROSIM(playPaused=True,
playBlind=False,
evalTime=eval_time,
xyz=(0, -2, 3),
hpr=(90, -30, 0))
offset = robot.Send_To_Simulator(sim, eval_time=eval_time)
env.Send_To_Simulator(sim, ID_offset=offset[0])
sim.Start()
sim.Wait_To_Finish()
sensor_data[i] = sim.Get_Results()
print sensor_data
return sensor_data
IDs = quad.Send_To_Simulator(sim3)
print IDs
IDs = mutant.Send_To_Simulator(sim3,
objID=IDs[0],
jointID=IDs[1],
sensorID=IDs[2],
neuronID=IDs[3])
sim3.Start()
sim3.Wait_To_Finish()
data = sim3.Get_Results()
print data[4, 1, T - 1], data[9, 1, T - 1]
if __name__ == "__main__":
#_Test_Draw()
import numpy as np
import environments
import fitness_functions as ff
t = Treebot.MC()
#t = Treebot.Non_Modular()
#t = Treebot()
sim = PYROSIM(playPaused=False, playBlind=False, evalTime=100)
offset = t.Send_To_Simulator(sim, eval_time=100)
env = environments.Cluster_Env.Bi_Sym(2, 2, 4, 2)
env.Send_To_Simulator(sim, offset[0])
sim.Start()
sim.Wait_To_Finish()
results = sim.Get_Results()
print ff.Treebot(results, env)
def run_experiment(
generator_fcn,
fitness_fcn,
name,
trial_num,
pop_size,
gens,
eval_time,
env_order,
dimensions,
env_space=False,
fitness_threshold=False,
development_layers=1,
):
time_stamp = dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
data = {}
envs_to_train_in = [env_space[i] for i in env_order]
evolver = evolvers.AFPO(max_population_size=pop_size,
generator_fcn=generator_fcn,
fitness_fcn=fitness_fcn,
environments=envs_to_train_in,
development_layers=development_layers,
max_generations=gens,
eval_time=eval_time,
fitness_threshold=fitness_threshold)
data['pop_size'] = evolver.max_population_size
#data['fitness_function'] = fitness_function
data['development_layers'] = evolver.development_layers
data['eval_time'] = evolver.eval_time
evolved_data = evolver.Evolve()
gens = evolver.generation - 1
data['gens'] = gens
best_robot = evolved_data[gens]['pareto_front'][0]['genome']
data['data'] = evolved_data
data['best_robot'] = best_robot
data['motor_speed'] = robots.MOTOR_SPEED
data['environments'] = envs_to_train_in
data['env_space'] = env_space
data['name'] = name
data['threshold'] = fitness_threshold
data['dimensions'] = dimensions
data['num_trained_in'] = len(envs_to_train_in)
env_fitness = [0] * 16
for index, env in enumerate(env_space):
sim = PYROSIM(playBlind=True, playPaused=False, evalTime=eval_time)
ids = best_robot.Send_To_Simulator(sim, eval_time=eval_time)
env.Send_To_Simulator(sim, ID_offset=ids[0])
sim.Start()
sim.Wait_To_Finish()
sensor_data = sim.Get_Results()
env_fitness[index] = fitness_fcn(sensor_data, env)
data_folder = './Data'
file_name = name + '_' + str(trial_num) + '_' + str(
len(env_order)) + '_' + str(dimensions) + '_' + str(time_stamp)
print file_name
pickle_file_name = file_name + '.pickle'
json_file_name = file_name + '.json'
if not os.path.isdir(data_folder):
os.makedirs(data_folder)
pickle_path_to_file = data_folder + '/' + pickle_file_name
json_path_to_file = data_folder + '/' + json_file_name
with open(pickle_path_to_file, 'w') as f:
pickle.dump(data, f)
#Create json daat
json_data = {}
print 'env fit', env_fitness
print 'env order', env_order
#store json data
json_data['env_fitness'] = env_fitness
json_data['type'] = name
json_data['threshold'] = fitness_threshold
json_data['dimensions'] = dimensions
json_data['num_trained_in'] = len(envs_to_train_in)
json_data['env_order'] = env_order
#Write json
with open(json_path_to_file, 'w') as f:
json.dump(json_data, f)
