def __init__(self, env):
        EpisodicTask.__init__(self, env)
        #Overall maximal tourque - is multiplied with relative max tourque for individual joint.
        self.maxPower = 100.0
        self.reward_history = []
        self.count = 0 #timestep counter
        self.epiLen = 1500 #suggestet episodic length for normal Johnnie tasks
        self.incLearn = 0 #counts the task resets for incrementall learning
        self.env.FricMu = 20.0 #We need higher friction for CCRL
        self.env.dt = 0.002 #We also need more timly resolution

        # normalize standard sensors to (-1, 1)
        self.sensor_limits = []
        #Angle sensors
        for i in range(self.env.actLen):
            self.sensor_limits.append((self.env.cLowList[i], self.env.cHighList[i]))
        # Joint velocity sensors
        for i in range(self.env.actLen):
            self.sensor_limits.append((-20, 20))
        #Norm all actor dimensions to (-1, 1)
        self.actor_limits = [(-1, 1)] * env.actLen
        self.oldAction = zeros(env.actLen, float)
        self.dist = zeros(9, float)
        self.dif = array([0.0, 0.0, 0.0])
        self.target = array([-6.5, 1.75, -10.5])
        self.grepRew = 0.0
        self.tableFlag = 0.0
        self.env.addSensor(SpecificBodyPositionSensor(['objectP00'], "glasPos"))
        self.env.addSensor(SpecificBodyPositionSensor(['palmLeft'], "palmPos"))
        self.env.addSensor(SpecificBodyPositionSensor(['fingerLeft1'], "finger1Pos"))
        self.env.addSensor(SpecificBodyPositionSensor(['fingerLeft2'], "finger2Pos"))
        #we changed sensors so we need to update environments sensorLength variable
        self.env.obsLen = len(self.env.getSensors())
        #normalization for the task spezific sensors
        for i in range(self.env.obsLen - 2 * self.env.actLen):
            self.sensor_limits.append((-4, 4))
        self.actor_limits = None
dataset/ETHPy150Open pybrain/pybrain/pybrain/rl/environments/ode/tasks/ccrl.py/CCRLTask.__init__/original