def actions(self, context: Context = None) -> Actions:
if self.__stage == self.UNASSIGNED:
raise Exception("%s: I can\'t act in stage unassigned." % self.name)
if self.__stage == self.CENTRALIZED_LEARNING:
if self.__round_index > 0:
raise Exception("Expected centralized learning in round 0. Got %d." %
self.__round_index)
if self.__central_algo.get_total_pulls(
) >= self.__num_pulls_per_round[0]:
# Early stop the centralized algorithm when it uses more than horizon
# / 2 pulls.
self.__stage = self.LEARNING
self.__arm_to_broadcast = np.random.choice(self.__assigned_arms)
self.__round_index += 1
return self.actions()
if len(self.__assigned_arms) == 1:
self.__stage = self.LEARNING
self.__arm_to_broadcast = self.__assigned_arms[0]
self.__round_index += 1
return self.actions()
central_algo_actions = self.__central_algo.actions()
if not central_algo_actions.arm_pulls:
# Centralized algorithm terminates before using up horizon / 2 pulls
self.__stage = self.LEARNING
self.__arm_to_broadcast = self.__central_algo.best_arm
self.__round_index += 1
return self.actions()
return central_algo_actions
elif self.__stage == self.LEARNING:
actions = Actions()
arm_pull = actions.arm_pulls.add()
arm_pull.arm.id = self.__arm_to_broadcast
arm_pull.times = self.__num_pulls_per_round[self.__round_index]
return actions
elif self.__stage == self.COMMUNICATION:
actions = Actions()
actions.state = Actions.WAIT
return actions
else:
# self.__stage == self.TERMINATION
actions = Actions()
actions.state = Actions.STOP
return actions
def actions(self, context=None) -> Actions:
# a core assumption is all non-empty actions immediately receive feedback
# and hence stage is changed here and not when feedback is received
del context
if self.__stage == "unassigned":
raise Exception("No arms assigned to agent " + self.name)
# in preparation:
# if only one arm is assigned, proceed to learning
# else if central_algo is running, forward its actions
# and get best arm when central_algo completes
# but interrupt central algo after T/2 pulls
elif self.__stage == "preparation":
if len(self.__assigned_arms) == 1:
self.__stage = "learning"
self.__learning_arm = self.__assigned_arms[0]
return self.actions()
if self.__central_algo.get_total_pulls() >= self.__horizon // 2:
self.__stage = "learning"
# use whatever best_arm the central algo outputs
self.__learning_arm = self.__central_algo.best_arm
return self.actions()
central_algo_actions = self.__central_algo.actions()
if not central_algo_actions.arm_pulls:
# central algo terminated before T/2 pulls
self.__stage = "learning"
self.__learning_arm = self.__central_algo.best_arm
return self.actions()
self.__central_algo_action_taken = True
return central_algo_actions
# in learning:
# if learning_arm is none, do no pulls and move to communication
# else pull learning_arm and move to communication
elif self.__stage == "learning":
actions = Actions()
self.__stage = "communication"
if self.__learning_arm is None:
actions.state = Actions.WAIT
return actions
else:
arm_pull = actions.arm_pulls.add()
arm_pull.arm.id = self.__learning_arm # pylint: disable=protobuf-type-error
arm_pull.times = self.__num_pulls_learning
return actions
elif self.__stage == "communication":
actions = Actions()
actions.state = Actions.WAIT
return actions
elif self.__stage == "termination":
actions = Actions()
actions.state = Actions.STOP
return actions
else:
raise Exception(self.name + ": " + self.__stage +
" does not allow actions to be played")
