Python EpisodicTask.performAction方法代码示例

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 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)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
    def performAction(self, action):
        action = self.action_from_joint_angles(action)

        # Carry out the action based on angular velocities
        EpisodicTask.performAction(self, action)

        return

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 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)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 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()

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 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)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 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)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 def performAction(self, action):
     self.t += 1
     EpisodicTask.performAction(self, action)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 def performAction(self, action):
     EpisodicTask.performAction(self, action)
     self.action = action

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
	def performAction(self, action):
	
		self.oldDist = self.__getDistance()		# copy current distance for shaping reward
		EpisodicTask.performAction(self, action)

		self.count += 1

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 def performAction(self, action):
     EpisodicTask.performAction(self, action)
     self.t += self.dt
     self.appendLog()

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 def performAction(self, action):
     EpisodicTask.performAction(self, action*self.maxPower)

# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import performAction [as 别名]
 def performAction(self, actions):
     self.t += 1
     actions = clip(actions, -0.05, 0.05) # set speed limit
     EpisodicTask.performAction(self, actions)