def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
# current time step
self.__time = 1
def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
self.__best_arm = None
self.__last_round = False
def update(self, feedback: Feedback):
if self.__stage == 'main_loop':
for arm_feedback in feedback.arm_feedbacks:
self.__active_arms[arm_feedback.arm.id].update(
np.array(arm_feedback.rewards))
# Initialization of median elimination
self.__stage = 'median_elimination'
# self.__me_ell = 1
self.__me_eps_ell = self.__eps_r / 8
self.__me_log_delta_ell = self.__log_delta_r - math.log(2)
self.__me_eps_left = self.__eps_r / 2
self.__me_delta_left = math.exp(self.__log_delta_r)
self.__me_active_arms = dict()
for arm_id in self.__active_arms:
self.__me_active_arms[arm_id] = PseudoArm()
elif self.__stage == 'median_elimination':
for arm_feedback in feedback.arm_feedbacks:
self.__me_active_arms[arm_feedback.arm.id].update(
np.array(arm_feedback.rewards))
if len(self.__me_active_arms) > self.__threshold:
median = np.median(
np.array([
pseudo_arm.em_mean
for (arm_id, pseudo_arm) in self.__me_active_arms.items()
]))
for (arm_id, pseudo_arm) in list(self.__me_active_arms.items()):
if pseudo_arm.em_mean < median:
del self.__me_active_arms[arm_id]
self.__me_eps_left *= 0.75
self.__me_delta_left *= 0.5
self.__me_eps_ell *= 0.75
self.__me_log_delta_ell -= math.log(2)
# self.__me_ell += 1
else:
# Best arm returned by median elimination
best_arm_by_me = argmax_or_min_tuple([
(pseudo_arm.em_mean, arm_id)
for arm_id, pseudo_arm in self.__me_active_arms.items()
])
# Second half of 'main_loop'
# Use estimated epsilon-best-arm to do elimination
for (arm_id, pseudo_arm) in list(self.__active_arms.items()):
if pseudo_arm.em_mean < self.__active_arms[
best_arm_by_me].em_mean - self.__eps_r:
del self.__active_arms[arm_id]
if len(self.__active_arms) == 1:
self.__best_arm = list(self.__active_arms.keys())[0]
self.__stage = 'main_loop'
self.__round += 1
self.__eps_r /= 2
self.__log_delta_r = math.log(
(1 - self.confidence) / 50) - 3 * math.log(self.__round)
def reset(self):
self.__active_arms: Dict[int, PseudoArm] = dict()
for arm_id in range(self.arm_num):
self.__active_arms[arm_id] = PseudoArm()
self.__budget_left = self.budget
self.__best_arm = None
self.__total_rounds = math.ceil(math.log(self.arm_num, 2))
# Current round
# self.__round = 1
self.__stop = False
def reset(self): self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num)] # Parameters suggested by the paper self.__beta = 0.5 self.__a = 1 + 10 / self.arm_num self.__eps = 0 self.__delta = (1 - self.confidence) / 5 # Total number of pulls used self.__total_pulls = 0 self.__stage = 'initialization' self.__ucb = np.array([0.0] * self.arm_num)
def reset(self):
self.__active_arms: Dict[int, PseudoArm] = dict()
for arm_id in range(self.arm_num):
self.__active_arms[arm_id] = PseudoArm()
self.__best_arm = None
# Current round index
self.__round = 1
self.__stage = 'main_loop'
# Main loop variables
self.__eps_r = 0.125
self.__log_delta_r = math.log((1 - self.confidence) / 50)
def reset(self):
# create only as many local arms as num_assigned_arms
# entire algo behaves as if there are just num_assigned_arms in the bandit
self.__pseudo_arms = [PseudoArm() for arm_id in self.__assigned_arms]
# Parameters suggested by the paper
self.__beta = 0.5
self.__a = 1 + 10 / len(self.__assigned_arms)
self.__eps = 0
self.__delta = (1 - self.confidence) / 5
# Total number of pulls used
self.__total_pulls = 0
self.__stage = 'initialization'
self.__ucb = np.array([0.0] * len(self.__assigned_arms))
def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
self.__active_arms = list(range(self.arm_num()))
self.__budget_left = self.budget()
self.__best_arm = None
self.__total_rounds = math.ceil(math.log(self.arm_num(), 2))
# current round
self.__round = 1
self.__last_round = False
def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
# parameters suggested by the paper
self.__beta = 0.5
self.__a = 1 + 10 / self.arm_num()
self.__eps = 0
self.__delta = (1 - self.confidence()) / 5
# total number of pulls used
self.__total_pulls = 0
self.__stage = 'initialization'
def update(self, feedback: Feedback):
for arm_feedback in feedback.arm_feedbacks:
self.__active_arms[arm_feedback.arm.id].update(
np.array(arm_feedback.rewards))
self.__budget_left -= len(arm_feedback.rewards)
if self.__stop:
self.__best_arm = argmax_or_min_tuple([
(arm.em_mean, arm_id) for arm_id, arm in self.__active_arms.items()
])
else:
# Remove half of the arms with the worst empirical means
remaining_arms = sorted(
self.__active_arms.items(), key=lambda x: x[1].em_mean,
reverse=True)[:math.ceil(len(self.__active_arms) / 2)]
self.__active_arms = dict((x, PseudoArm()) for x, _ in remaining_arms)
def median_elimination(self) -> List[Tuple[int, int]]:
"""
Returns:
arms to pull in median elimination
"""
self.__me_pseudo_arms = [(arm_id, PseudoArm())
for arm_id in self.__me_active_arms]
if len(self.__me_active_arms) <= self.__threshold:
# uniform sampling
pulls = math.ceil(0.5 / (self.__me_eps_left**2) * (math.log(
2 / self.__me_delta_left / len(self.__me_active_arms))))
else:
pulls = math.ceil(4 / (self.__me_eps_ell**2) *
(math.log(3) - self.__me_log_delta_ell))
actions = [(arm_id, pulls) for arm_id in self.__me_active_arms]
return actions
def reset(self):
# Calculate pulls assigned to each arm per round
self.__pulls_per_round = [-1]
nk = [0]
for k in range(1, self.arm_num):
nk.append(
math.ceil(1 / self.__bar_log_K * (self.budget - self.arm_num) /
(self.arm_num + 1 - k)))
self.__pulls_per_round.append(nk[k] - nk[k - 1])
self.__active_arms: Dict[int, PseudoArm] = dict()
for arm_id in range(self.arm_num):
self.__active_arms[arm_id] = PseudoArm()
self.__budget_left = self.budget
self.__best_arm = None
# Current round
self.__round = 1
def reset(self):
"""Reset the learner
.. warning::
This function should be called before the start of the game.
"""
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num())]
# calculate pulls_per_round
self.__pulls_per_round = [-1]
nk = [0]
for k in range(1, self.arm_num()):
nk.append(
math.ceil(1 / self.__bar_log_K * (self.budget() - self.arm_num()) /
(self.arm_num() + 1 - k)))
self.__pulls_per_round.append(nk[k] - nk[k - 1])
self.__active_arms = set(range(self.arm_num()))
self.__budget_left = self.budget()
self.__best_arm = None
# current round
self.__round = 1
def actions(self, context: Context) -> Actions:
if len(self.__active_arms) == 1:
return Actions()
actions: Actions
if self.__stage == 'main_loop':
actions = Actions()
for arm_id in self.__active_arms:
self.__active_arms[arm_id] = PseudoArm()
pulls = math.ceil(2 / (self.__eps_r**2) *
(math.log(2) - self.__log_delta_r))
for arm_id in self.__active_arms:
arm_pull = actions.arm_pulls.add()
arm_pull.arm.id = arm_id
arm_pull.times = pulls
else:
# self.__stage == 'median_elimination'
actions = self.__median_elimination()
return actions
def actions(self, context=None) -> Optional[List[Tuple[int, int]]]:
"""
Args:
context: context of the ordinary bandit which should be `None`
Returns:
arms to pull
"""
if len(self.__active_arms) == 1:
self.__last_actions = None
elif self.__stage == 'main_loop':
self.__pseudo_arms = [(arm_id, PseudoArm())
for arm_id in self.__active_arms]
pulls = math.ceil(2 / (self.__eps_r**2) *
(math.log(2) - self.__log_delta_r))
self.__last_actions = [(arm_id, pulls)
for arm_id in self.__active_arms]
else:
# self.__stage == 'median_elimination'
self.__last_actions = self.median_elimination()
return self.__last_actions
def __median_elimination(self) -> Actions:
"""
Returns:
arms to pull in median elimination
"""
actions = Actions()
for arm_id in self.__me_active_arms:
self.__me_active_arms[arm_id] = PseudoArm()
if len(self.__me_active_arms) <= self.__threshold:
# Uniform sampling
pulls = math.ceil(
0.5 / (self.__me_eps_left**2) *
(math.log(2 / self.__me_delta_left / len(self.__me_active_arms))))
else:
pulls = math.ceil(4 / (self.__me_eps_ell**2) *
(math.log(3) - self.__me_log_delta_ell))
for arm_id in self.__me_active_arms:
arm_pull = actions.arm_pulls.add()
arm_pull.arm.id = arm_id
arm_pull.times = pulls
return actions
def reset(self): self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num)] self.__best_arm = None self.__stop = False
def reset(self):
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num)]
# Current time step
self.__time = 1
def reset(self):
self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num)]