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from pyrobot.brain import Brain
from pyrobot.brain.VisConx.VisRobotConx import *
import pickle
import pyrobot.brain.ravq
import os
import time
import random
# log file directories
rootDirectory = "/local/"
currentExperiment = "data2/"
currentBrain = "/local/GovernorBrainTest.py"
class GovernorBrain(Brain):
"""A brain that uses a RAVQ to govern network learning."""
def setup(self):
# for use with player/stage
#self.startService('truth')
#self.startService('bumper')
# robot parameters
self.robot.range.units = 'ROBOTS'
self.maxvalue = self.robot.range.getMaxvalue()
self.maxvalue += 0.10
# status variables
self.verbosity = 1
self.direction = 1
self.blockedFront = 0
self.wasStalled = 0
self.counter = 0
self.previous = [0.0, 0.0]
# tweakable params
self.sleepTime = 0.05
self.stopTime = 10000
# choose the governor method
self.method = 0
# file IO
self.path = rootDirectory + currentExperiment
# create network
self.net = VisRobotNetwork() # could use VisRobotSRN()
self.inSize = self.robot.range.count
self.net.addLayers(self.inSize, self.inSize/2, 2)
self.net.loadWeightsFromFile(self.path + 'network.wts')
# defaults - but here explicit
self.net.setBatch(0)
self.net.setInteractive(0)
self.net.setVerbosity(0)
# learning parameters
self.net.setEpsilon(0.2)
self.net.setMomentum(0.9)
self.net.setTolerance(0.05)
# set learning (no learning during testing)
self.net.setLearning(0)
# input ravq (tweakable parameters)
fp = open(self.path + 'ravq.pck')
self.ravq = pickle.load(fp)
fp.close()
self.ravq.setHistory(0)
self.ravq.setAddModels(0)
self.ravq.setLearning(0)
self.ravq.setMask([1] * self.inSize + [self.inSize / 2] * 2)
self.ravq.autoLabel()
def destroy(self):
self.net.destroy()
def scaleSensors(self, val):
"""From Robots (or anything) to [0, 1]"""
return (val / self.maxvalue)
def scaleMotors(self, val):
"""[-1, 1] to [0, 1]"""
return (val + 1) / 2.0
def kick(self):
"""How to get unstuck."""
self.wasStalled += 1
self.move(1.0 * random.random(), 0.0)
time.sleep(1)
self.update()
if self.robot.stall:
self.move(-1.0 * random.random(), 0.0)
time.sleep(1)
self.update()
if self.robot.stall:
self.move(0.0, 1.0 * random.random())
time.sleep(1)
self.update()
if self.robot.stall:
self.move(0.0, -1.0 * random.random())
time.sleep(1)
self.update()
# this is not the wall follower!
def avoidObstacles(self):
"""
Determines next action, but doesn't execute it.
Returns the translate and rotate values.
When front is blocked, it picks to turn away from the
direction with the minimum reading and maintains that
turn until front is clear.
"""
d = 0.7
ds = 0.3
turn = random.random()
minFront = min([s.value for s in self.robot.range["front"]])
minLeft = min([s.value for s in self.robot.range["front-left"]])
minRight = min([s.value for s in self.robot.range["front-right"]])
sideLeft = self.robot.range[0].value
sideRight = self.robot.range[7].value
if minFront < d:
if not self.blockedFront:
if minRight < minLeft:
self.direction = 1
else:
self.direction = -1
self.blockedFront = 1
return [0, self.direction * turn]
elif minLeft < d:
if self.blockedFront:
return [0, self.direction * turn]
else:
return [0,-turn]
elif minRight < d:
if self.blockedFront:
return [0, self.direction * turn]
else:
return [0,turn]
else:
if sideLeft < ds:
return [0,-turn]
elif sideRight < ds:
return [0,turn]
else:
self.blockedFront = 0
return [.2,0]
def wallFollower(self):
# tweakable parameters
frontRange = 0.7
minRange = .5
maxRange = .7
amount = 0.1
# important sensors
minFront = min(self.get('robot/range/front/value'))
minLeft = min(self.get('robot/range/front-left'))
minRight = min(self.get('robot/range/front-right/value'))
left = min(self.get('robot/range/left/value'))
right = min(self.get('robot/range/right/value'))
self.score += left
# the decision algorithm
if minFront < frontRange:
if not self.blockedFront:
self.direction = -1
self.blockedFront = 1
return [0, self.direction * amount]
else:
self.blockedFront = 0
if minLeft < minRange:
if self.blockedFront:
return [0, self.direction * amount]
else:
return [amount/2.0, -amount]
elif minLeft > maxRange:
if self.blockedFront:
return [0, self.direction * amount]
else:
return [amount/2.0, amount]
elif minRight < minRange:
if self.blockedFront:
return [0, self.direction * amount]
else:
return [amount, amount]
else:
self.blockedFront = 0
return [0.1, 0.0]
def step(self):
# display count
if self.verbosity > 0: print self.counter
if self.counter > self.stopTime:
self.destroy() # closes files
self.pleaseStop()
# use self.avoidObstacles() to change primitive behavior
motors = self.avoidObstacles()
# scale values that the network will use
inputs = map(self.scaleSensors, self.get('robot/range/all/value'))
targets = map(self.scaleMotors, motors)
# classify the data using the ravq
self.ravq.input(inputs + targets)
if self.verbosity > 0:
print " RAVQ Winner: ", self.ravq.getLabel(self.ravq.winner)
print " Number of Models: ", len(self.ravq.models)
# kick if things get bad
if self.get('robot/stall'):
print "Kicking"
self.kick()
error, correct, total = self.net.step(input = inputs, output = targets)
# move the robot according to the network
self.move((self.net.getLayer('output').getActivations()[0] * 2.0) - 1.0,\
(self.net.getLayer('output').getActivations()[1] * 2.0) - 1.0)
# sleep, record motor values, increment counter
time.sleep(self.sleepTime)
# optional additional input of motor values
self.previous = motors[:]
self.counter += 1
def INIT(engine):
return GovernorBrain('GovernorBrain', engine)
if __name__ == '__main__':
os.system("pyro -r Khepera -b /local/GovernorBrainTest.py") |
