epsilon = 1.0
epsilon_min = 0.1
alpha = 0.001
gamma = 0.01
decay = 0.9
replace_target_iter = 1
memory_size = 200
batch_size = 32
use_seed = True
training_seed = 1
test_seed = 1
debug_print = True
'___ENVIRONMENT PARAMETERS___'
environment_parameters = Parameters()
'___CREATE TEST FOLDER___'
path = create_test_folder('ai')
writeParameterToFile(environment_parameters, training_episodes,
testing_episodes, epsilon, epsilon_min, alpha, gamma,
decay, replace_target_iter, memory_size, batch_size,
use_seed, path, test_seed)
if (debug_print == True):
print("Parameter written to Test folder.")
'___DATA GENERATION___'
generator = DataGenerator(use_seed, environment_parameters)
training_data = generator.generateDataSet(training_episodes, training_seed)
test_data = generator.generateDataSet(testing_episodes, test_seed)
if (debug_print == True):
# import time
#
# start_time = time.time()
# for i in xrange(100000):
# a = other_agents.get_sjf_action(env.machine, env.job_slot)
# env.step(a)
# end_time = time.time()
# print
# "- Elapsed time: ", end_time - start_time, "sec -"
if __name__ == '__main__':
# test_backlog()
# test_compact_speed()
# test_image_speed()
pa = Parameters()
pa.simu_len = 20
pa.num_res = 2
env = Env(pa)
env.step(5,0)
env.step(5,0)
env.step(5,1)
env.step(5,1)
env.step(5,1)
env.step(1,1)
env.step(2,0)
env.step(1,0)
env.step(5,0)
# env.step(0)
def __init__(self, numIt, nPop, filename):
it = 0
filename = os.getcwd() + '/' + filename
# Read input file
if rank == 0:
self.parameters = Parameters(filename)
self.temperatures = Temperatures(filename)
self.simulation = Simulation(filename)
self.psoparameters = ParticleSwarmParameters(filename)
else:
self.parameters = None
self.temperatures = None
self.simulation = None
self.psoparameters = None
self.parameters = comm.bcast(self.parameters, root=0)
self.temperatures = comm.bcast(self.temperatures, root=0)
self.simulation = comm.bcast(self.simulation, root=0)
self.psoparameters = comm.bcast(self.psoparameters, root=0)
# Initilize some variables
number_of_temperatures = self.temperatures.GetDim()
w = self.psoparameters.w
c1 = self.psoparameters.c1
c2 = self.psoparameters.c2
if rank == 0:
swarm = [
Particle(self.parameters, self.temperatures, self.simulation)
for i in range(nPop)
]
else:
swarm = None
if rank == 0:
Utility.LogMessage('Scattering the initial swarm')
particle = comm.scatter(swarm, root=0)
if rank == 0:
Utility.LogMessage('Initializing the costs for the swarm')
particle.Evaluate(it)
particle.UpdateBestPosition()
comm.barrier()
if rank == 0:
Utility.LogMessage('Gathering all the costs back to node 0')
swarm = comm.gather(particle, root=0)
if rank == 0:
length = len(swarm)
for i in range(length):
if i % number_of_temperatures == 0:
Utility.PrintCoordinates(it, swarm[i])
if rank == 0:
best_particle = Utility.GetBestParticle(swarm,
number_of_temperatures)
Utility.LogMessage('Best global cost: {}, {}, {}, {}'.format(
best_particle.cost, best_particle.pars, best_particle.pos,
best_particle.dens))
it += 1
while it <= numIt:
if rank == 0:
Utility.LogMessage('Starting iteration {}'.format(it))
for i in range(len(swarm)):
if i % number_of_temperatures == 0:
p = swarm[i]
p.CalculateNextVelocity(w, c1, c2, best_particle.pos)
p.CalculateNextPosition()
particle = comm.scatter(swarm, root=0)
particle.Evaluate(it)
particle.UpdateBestPosition()
swarm = comm.gather(particle, root=0)
if rank == 0:
length = len(swarm)
for i in range(length):
if i % number_of_temperatures == 0:
Utility.PrintCoordinates(it, swarm[i])
if rank == 0:
best_p = Utility.GetBestParticle(swarm, number_of_temperatures)
if best_p.cost < best_particle.cost:
best_particle = best_p
Utility.LogMessage(
'Found better global cost: {}, {}, {}'.format(
best_particle.cost, best_particle.pos,
best_particle.dens))
else:
Utility.LogMessage(
'Old global best is still better! {}, {}, {}'.format(
best_particle.cost, best_particle.pos,
best_particle.dens))
it += 1
def __init__(self, numIt, nPop, filename):
it = 0
# Read input file
if rank == 0:
self.parameters = Parameters(filename)
self.temperatures = Temperatures(filename)
self.system = System(filename)
self.psoparameters = ParticleSwarmParameters(filename)
self.charges = Charges(filename)
else:
self.parameters = None
self.temperatures = None
self.system = None
self.psoparameters = None
self.charges = None
self.parameters = comm.bcast(self.parameters, root=0)
self.temperatures = comm.bcast(self.temperatures, root=0)
self.system = comm.bcast(self.system, root=0)
self.psoparameters = comm.bcast(self.psoparameters, root=0)
self.charges = comm.bcast(self.charges, root=0)
# Equilibrate all simulations
if rank == 0:
Utility.LogMessage('Generate files for equilibrium')
Utility.GenerateFilesForEquilibrate(self.temperatures,
self.parameters, self.system,
self.charges)
Utility.LogMessage('Done generating equilibrium files')
Utility.LogMessage('Running equilibrium simulations')
comm.barrier()
Utility.RunEquilibrate(self.temperatures)
comm.barrier()
if rank == 0:
Utility.LogMessage('Done equilibrating simulations')
# Initilize some variables
dim = self.parameters.GetDim()
w = self.psoparameters.w
c1 = self.psoparameters.c1
c2 = self.psoparameters.c2
if rank == 0:
swarm = [
Particle(self.parameters, self.temperatures)
for i in range(nPop)
]
else:
swarm = None
if rank == 0:
Utility.LogMessage('Scattering the initial swarm')
particle = comm.scatter(swarm, root=0)
if rank == 0:
Utility.LogMessage('Initializing the costs for the swarm')
particle.Evaluate(it)
particle.UpdateBestPosition()
comm.barrier()
if rank == 0:
Utility.LogMessage('Gathering all the costs back to node 0')
swarm = comm.gather(particle, root=0)
if rank == 0:
length = len(swarm)
for i in range(length):
if i % 3 == 0:
p = swarm[i]
Utility.PrintCoordinates(it, p)
if rank == 0:
best_particle = Utility.GetBestParticle(swarm)
Utility.LogMessage('Best global cost: {}, {}, {}, {}'.format(
best_particle.cost, best_particle.pars, best_particle.pos,
best_particle.dens))
it += 1
while it <= numIt:
if rank == 0:
Utility.LogMessage('Starting iteration {}'.format(it))
for i in range(len(swarm)):
if i % 3 == 0:
p = swarm[i]
p.CalculateNextVelocity(w, c1, c2, best_particle.pos)
p.CalculateNextPosition()
particle = comm.scatter(swarm, root=0)
particle.Evaluate(it)
particle.UpdateBestPosition()
swarm = comm.gather(particle, root=0)
if rank == 0:
length = len(swarm)
for i in range(length):
if i % 3 == 0:
p = swarm[i]
Utility.PrintCoordinates(it, p)
if rank == 0:
best_p = Utility.GetBestParticle(swarm)
if best_p.cost < best_particle.cost:
best_particle = best_p
Utility.LogMessage(
'Found better global cost: {}, {}, {}'.format(
best_particle.cost, best_particle.pos,
best_particle.dens))
else:
Utility.LogMessage(
'Old global best is still better! {}, {}, {}'.format(
best_particle.cost, best_particle.pos,
best_particle.dens))
it += 1
