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#VisRobotBrain.py
from pyrobot.brain import Brain
from pyrobot.brain.vconx import *
from pyrobot.gui.plot.scatter import *
from pyrobot.gui.plot.hinton import *
from os import system, unlink
def a2s(a):
""" array to string """
retval = ''
for i in range(len(a)):
retval += "%.5f " % a[i]
return retval
class NNBrain(Brain):
"""
This is an example brain controlled by a neural network.
This simple example loads the range sensor data into the
input layer, and trains the network to stay away from
things.
"""
def setup(self):
""" Create the network. """
self.net = VINetwork()
self.hiddenLayerSize = 10
self.net.addLayers(self.robot.range.count,
self.hiddenLayerSize, 2)
self.net.setBatch(0)
self.net.initialize()
self.net.setEpsilon(0.5)
self.net.setMomentum(.1)
self.net.setLearning(1)
self.counter = 0
self.maxvalue = self.robot.range.getMaxvalue()
def destroy(self):
self.net.destroy()
def scale(self, val):
return (val / self.maxvalue)
def step(self):
# First, set inputs and targets:
ins = [self.scale(s.value) for s in self.robot.range["all"]]
# Compute targets:
target_rotate = 0.5
if min([s.value for s in self.robot.range["front"]]) < 1:
target_trans = 0.0
target_rotate = 0.0
elif min([s.value for s in self.robot.range["back"]]) < 1:
target_trans = 1.0
else:
target_trans = 1.0
if min([s.value for s in self.robot.range["left"]]) < 1:
target_rotate = 0.0
elif min([s.value for s in self.robot.range["right"]]) < 1:
target_rotate = 1.0
target = [target_trans, target_rotate]
# set inputs and targets with step
self.net.step( input = ins, output = target)
# get the output, and move:
trans = (self.net['output'].activation[0] - .5) / 2.0
rotate = (self.net['output'].activation[1] - .5) / 2.0
self.robot.move(trans, rotate)
self.counter += 1
def INIT(engine):
return NNBrain('NNBrain', engine) |
