""" 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 startGame(self):
UCB.startGame(self)
self.budget = self.inibudget
self.estmeans.fill(0.0)
self.successes.fill(0)
def startGame(self):
UCB.startGame(self)
SafeAlg.startGame(self)
def startGame(self):
UCB.startGame(self)
self.positive_mean = nb.repeat(0.0, self.nbArms, dtype='float')
self.negative_mean = nb.repeat(0.0, self.nbArms, dtype='float')
self.positive_count = nb.repeat(0, self.nbArms, dtype='int')
self.negative_count = nb.repeat(0, self.nbArms, dtype='int')
def startGame(self):
UCB.startGame(self)
self.reward_samples = [np.array([0.0]) for a in range(self.nbArms)]
def startGame(self):
UCB.startGame(self)
Budgeted.startGame(self)
BernoulliEstimator.startGame(self)