def performAction(self, action):
""" POMDP tasks, as they have discrete actions, can me used by providing either an index,
or an array with a 1-in-n coding (which can be stochastic). """
if type(action) == ndarray:
action = drawIndex(action, tolerant = True)
self.steps += 1
EpisodicTask.performAction(self, action)
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Johnnie task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints=self.env.getSensorByName('JointSensor') #The joint angles
isSpeeds=self.env.getSensorByName('JointVelocitySensor') #The joint angular velocitys
act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
action=tanh((act-isJoints-isSpeeds)*16.0)*self.maxPower*self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
def performAction(self, action):
""" a filtered mapping towards performAction of the underlying environment. """
# scaling
self.incStep()
action = (action + 1.0) / 2.0 * self.dif + self.env.fraktMin * self.env.dists[0]
#Clipping the maximal change in actions (max force clipping)
action = clip(action, self.action - self.maxSpeed, self.action + self.maxSpeed)
EpisodicTask.performAction(self, action)
self.action = action.copy()
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard CCRL task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
self.oldAction = action
#Grasping as reflex depending on the distance to target - comment in for more easy grasping
#if abs(self.dist[2])<2.0: action[15]=(1.0+2.0*action[15])*.3333 #self.grepRew=action[15]*.01
#else: action[15]=(-1.0+2.0*action[15])*.3333 #self.grepRew=action[15]*-.03
isJoints=array(self.env.getSensorByName('JointSensor')) #The joint angles
isSpeeds=array(self.env.getSensorByName('JointVelocitySensor')) #The joint angular velocitys
act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
action=tanh((act-isJoints-0.9*isSpeeds*self.env.tourqueList)*16.0)*self.maxPower*self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.action = action