def __init__(self,
rs,
sizeOfTarget,
thetafile,
arm="Arm26",
estim="Inv",
actor=None,
logDir="Best",
saveDir=None):
self.rs = rs
self.posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
self.arm = ArmType[arm]()
self.arm.setDT(rs.dt)
if (not self.det):
self.arm.setNoise(rs.noise)
if (rs.costClass == None):
self.trajCost = CostDDPG(rs)
else:
self.trajCost = rs.costClass(rs)
if (len(self.posIni.shape) == 1):
self.posIni = self.posIni.reshape((1, self.posIni.shape[0]))
if estim == "Inv":
self.stateEstimator = StateEstimator(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "Reg":
self.stateEstimator = StateEstimatorRegression(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
elif estim == "Hyb":
self.stateEstimator = StateEstimatorHyb(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "No":
self.stateEstimator = StateEstimatorNoFeedBack(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
else:
raise TypeError("This Estimator do not exist")
if (saveDir == None):
self.foldername = self.rs.OPTIpath + str(self.sizeOfTarget) + "/"
else:
self.foldername = saveDir
self.costStore = []
self.trajTimeStore = []
self.lastCoord = []
self.sizeOfTarget = sizeOfTarget
self.thetafile = thetafile
self.actor = actor
self.saveName = self.foldername + logDir + "/"
self.nbReset = 0
self.cost = 0
self.max = 0
self.progressTab = [0.1, 0.25, 0.5, 0.75, 1.]
self.progress = 1
self.reset()
