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):
""" 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):
""" 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):
""" 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):
self.t += 1
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
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.action = action