def train(self, transitionSamples):
print "Entrenando..."
k = 0
trainer = RPropMinusTrainer(self.Q, batchlearning=True)
#trainer = BackpropTrainer(self.Q, batchlearning=False)
TS = SupervisedDataSet(4, 1)
while (k < self._epochs):
if k % 10 == 0:
print "\t ", k
# Genero training set en base a las muestras
# Input: Vector de 4 dimensiones (angulo, vel.angular, pos, accion)
# Target: Valor
TS.clear()
for s, a, s_1, costo in transitionSamples:
# Tomo Q para s', para todas las acciones posibles
# (vector con el valor para s', para cada una de las 3 acciones posibles)
# Q_s1 = [ self.Q.activate([s_1.angulo, s_1.velocidadAngular, s_1.posicion, b]) for b in range(Accion.maxValor + 1) ]
valDerecha = self.Q.activate([
s_1.angulo, s_1.velocidadAngular, s_1.posicion,
Accion.DERECHA
])
valIzquierda = self.Q.activate([
s_1.angulo, s_1.velocidadAngular, s_1.posicion,
Accion.IZQUIERDA
])
if valDerecha >= 1 or valDerecha <= 0:
print "Q incorrecta: ", valDerecha
if valIzquierda >= 1 or valIzquierda <= 0:
print "Q incorrecta: ", valIzquierda
# Input y Target para la red neuronal
inputVal = (s.angulo, s.velocidadAngular, s.posicion, a)
if costo == 0:
targetVal = costo
else:
targetVal = costo + self._gamma * min(
valDerecha, valIzquierda)
if targetVal > 1 or targetVal < 0:
print "Target incorrecto: ", targetVal
TS.addSample(inputVal, targetVal)
# Entreno la red neuronal
trainer.setData(TS)
trainer.train() # 1 epoch
#trainer.trainEpochs(self._epochsNN)
k = k + 1
from utils import updateDataset,buildDataset,buildRecurrentNetwork,loadRecurrentNetwork
from pybrain.supervised.trainers.rprop import RPropMinusTrainer
from pybrain.tools.xml.networkwriter import NetworkWriter
from pybrain.tools.xml.networkreader import NetworkReader
#nn=buildRecurrentNetwork()
nn=loadRecurrentNetwork('recurrentNetwork.xml')
dataset=buildDataset()
trainer=RPropMinusTrainer(nn)
trainer.setData(dataset)
print 'dataset set for trainer'
trainer.trainUntilConvergence()
print 'trained to convergence'
NetworkWriter.writeToFile(nn,'recurrentNetwork.xml')
from utils import updateDataset, buildDataset, buildRecurrentNetwork, loadRecurrentNetwork
from pybrain.supervised.trainers.rprop import RPropMinusTrainer
from pybrain.tools.xml.networkwriter import NetworkWriter
from pybrain.tools.xml.networkreader import NetworkReader
#nn=buildRecurrentNetwork()
nn = loadRecurrentNetwork('recurrentNetwork.xml')
dataset = buildDataset()
trainer = RPropMinusTrainer(nn)
trainer.setData(dataset)
print 'dataset set for trainer'
trainer.trainUntilConvergence()
print 'trained to convergence'
NetworkWriter.writeToFile(nn, 'recurrentNetwork.xml')
