class Dagger():
""" In fact the Python twin of the
corresponding Java ForwardAgent.
"""
def __init__(self,initialTraining):
"""Constructor"""
self.initialTraining = initialTraining
self.States = []
self.Actions = []
self.learner = Learner()
def getName(self):
return 'Dagger'
def loadModel(self):
self.learner.Load()
def askForHelp(self,img):
return 1
def getAction(self,img):
""" Possible analysis of current observation and sending an action back
"""
action = self.learner.getAction(img)
self.actionTaken = action
return action
def integrateObservation(self, img,action):
"""This method stores the observation inside the agent"""
if (self.initialTraining or (self.actionTaken[0] != action[0] and self.action[0] != 2)):
img = cv2.pyrDown((cv2.pyrDown(img)))
winSize = (32,32)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,
histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
state = hog.compute(img)
self.States.append(state)
self.Actions.append(action)
#self.printLevelScene()
def updateModel(self):
States = np.array(self.States)
Actions = np.array(self.Actions)
self.learner.trainModel(States,Actions,fineTune = True)
def getDataAdded(self):
return self.dataAdded
def newModel(self):
states = np.array(self.States)
actions = np.array(self.Actions)
self.learner.trainModel(states,actions)
def getNumData(self):
return self.learner.getNumData()
def reset(self):
self.States = []
self.Actions = []
