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):
action = self.action_from_joint_angles(action)
# Carry out the action based on angular velocities
EpisodicTask.performAction(self, action)
return
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):
action = self.action_from_joint_angles(action)
# Carry out the action based on angular velocities
EpisodicTask.performAction(self, action)
return
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 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(abs(self.dist[:3]).sum())<2.0: action[15]=1.0 #self.grepRew=action[15]*.01
else: action[15]=-1.0 #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):
#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):
#Filtered mapping towards performAction of the underlying environment
#The standard Tetra2 task uses a PID controller to control directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints=self.env.getSensorByName('JointSensor') #The joint angles
#print "Pos:", [int(i*10) for i in isJoints]
isSpeeds=self.env.getSensorByName('JointVelocitySensor') #The joint angular velocitys
#print "Speeds:", [int(i*10) for i in isSpeeds]
#print "Action", action, "cHighList",self.env.cHighList , self.env.cLowList
#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)*self.maxPower*self.env.tourqueList #simple PID
#print "Action", act[:5]
EpisodicTask.performAction(self, action *self.maxPower*self.env.tourqueList)
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Tetra2 task uses a PID controller to control directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints = self.env.getSensorByName('JointSensor') #The joint angles
#print "Pos:", [int(i*10) for i in isJoints]
isSpeeds = self.env.getSensorByName(
'JointVelocitySensor') #The joint angular velocitys
#print "Speeds:", [int(i*10) for i in isSpeeds]
#print "Action", action, "cHighList",self.env.cHighList , self.env.cLowList
#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)*self.maxPower*self.env.tourqueList #simple PID
#print "Action", act[:5]
EpisodicTask.performAction(
self, action * self.maxPower * self.env.tourqueList)
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(abs(self.dist[:3]).sum()) < 2.0:
action[15] = 1.0 #self.grepRew=action[15]*.01
else:
action[15] = -1.0 #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, actions):
self.t += 1
actions = clip(actions, -0.05, 0.05) # set speed limit
EpisodicTask.performAction(self, actions)
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def performAction(self, action):
EpisodicTask.performAction(self, action*self.maxPower)
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.action = action
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.t += self.dt
self.appendLog()
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def performAction(self, action): self.oldDist = self.__getDistance() # copy current distance for shaping reward EpisodicTask.performAction(self, action) self.count += 1
