def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
self.budget += reward
self.estmeans[arm] = (self.estmeans[arm] *
(self.pulls[arm] - 1) + reward) / self.pulls[arm]
if (reward > 0):
self.successes[arm] += 1
def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
if (reward >= 0):
self.positive_count[arm] += 1
self.positive_mean[arm] = (self.positive_mean[arm] *
(self.positive_count[arm] - 1) +
reward) / self.positive_count[arm]
else:
self.negative_count[arm] += 1
self.negative_mean[arm] = (self.negative_mean[arm] *
(self.negative_count[arm] - 1) +
reward) / self.negative_count[arm]
""" Example of use of SMPyBandits.
See https://SMPyBandits.GitHub.io/API.html for more details!"""
import numpy as np
np.random.seed(0) # for reproducibility
from SMPyBandits.Arms import Bernoulli
arms = [Bernoulli(0.1), Bernoulli(0.9)]
from SMPyBandits.Environment import MAB
my_MAB_problem = MAB(arms)
nbArms = my_MAB_problem.nbArms # 2 arms !
from SMPyBandits.Policies import UCB
my_UCB_algo = UCB(nbArms)
my_UCB_algo.startGame() # reset internal memory
horizon = 1000
for t in range(horizon): # simulation loop
chosen_arm = my_UCB_algo.choice()
observed_reward = my_MAB_problem.draw(chosen_arm)
my_UCB_algo.getReward(chosen_arm, observed_reward)
cumulated_reward = sum(my_UCB_algo.rewards) # random!
number_of_plays = sum(my_UCB_algo.pulls) # horizon = 1000
mean_reward = cumulated_reward / number_of_plays
print("The UCB algorithm obtains here a mean reward =", mean_reward)
def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
self.budget += reward
def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
SafeAlg.getReward(self, arm, reward)
def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
self.reward_samples[arm] = np.sort(
np.append(self.reward_samples[arm], [reward]))
def getReward(self, arm, reward):
UCB.getReward(self, arm, reward)
Budgeted.getReward(self, reward)
BernoulliEstimator.getReward(self, arm, reward)