def exp3(self, numActions, gamma, rewardMin=0, rewardMax=1):
#weights = [1.0] * numActions
t = 0
while True:
probabilityDistribution = distr(self.weights, gamma)
choice = draw(probabilityDistribution)
self.choiceFreq[choice] += 1
#put choice in the queue
self.choice_queue.put(choice)
self.choice_queue.join()
#get reward from queue
theReward = self.reward_queue.get()
self.reward_queue.task_done()
#theReward = reward(choice, t)
scaledReward = (theReward - rewardMin) / (
rewardMax - rewardMin) # rewards scaled to 0,1
probChoice = float(self.choiceFreq[choice] + 1) / (t + 1)
#estimatedReward = 1.0 * scaledReward / probabilityDistribution[choice]
#estimatedReward = (1.0 * scaledReward) / probChoice
estimatedReward = 1.0 * scaledReward * self.distanceLastchoice(
t, choice)
self.weights[choice] *= math.exp(
estimatedReward * gamma /
numActions) # important that we use estimated reward here!
yield choice, theReward, estimatedReward, self.weights
t = t + 1
def exp3(self,numActions, gamma, rewardMin = 0, rewardMax = 1):
t = 0
while True:
probabilityDistribution = distr(self.weights, gamma)
choice = draw(probabilityDistribution)
self.choiceFreq[choice]+=1
#put choice in the queue
self.choice_queue.put(choice)
self.choice_queue.join()
#get reward from queue
theReward = self.reward_queue.get()
self.reward_queue.task_done()
#theReward = reward(choice, t)
scaledReward = (theReward - rewardMin) / (rewardMax - rewardMin) # rewards scaled to 0,1
#-case frequency
#probChoice=float(self.choiceFreq[choice] +1)/(t+1)
#estimatedReward = (1.0 * scaledReward) / probChoice
#-case distribution
#estimatedReward = 1.0 * scaledReward / probabilityDistribution[choice]
#-case distance
estimatedReward = 1.0 * scaledReward * self.distanceLastchoice(t,choice)
self.weights[choice] *= math.exp(estimatedReward * gamma / numActions) # important that we use estimated reward here!
yield choice, theReward, estimatedReward, self.weights
t = t + 1
def exp3(numActions, reward, gamma, rewardMin = 0, rewardMax = 1):
weights = [1.0] * numActions
t = 0
while True:
probabilityDistribution = distr(weights, gamma)
choice = draw(probabilityDistribution)
theReward = reward(choice, t)
scaledReward = (theReward - rewardMin) / (rewardMax - rewardMin) # rewards scaled to 0,1
estimatedReward = 1.0 * scaledReward / probabilityDistribution[choice]
weights[choice] *= math.exp(estimatedReward * gamma / numActions) # important that we use estimated reward here!
yield choice, theReward, estimatedReward, weights
t = t + 1
def exp3(numActions, reward, gamma, rewardMin=0, rewardMax=1):
weights = [1.0] * numActions
t = 0
while True:
probabilityDistribution = distr(weights, gamma)
choice = draw(probabilityDistribution)
theReward = reward(choice, t)
scaledReward = (theReward - rewardMin) / (rewardMax - rewardMin
) # rewards scaled to 0,1
estimatedReward = 1.0 * scaledReward / probabilityDistribution[choice]
weights[choice] *= math.exp(
estimatedReward * gamma /
numActions) # important that we use estimated reward here!
yield choice, theReward, estimatedReward, weights
t = t + 1
def simpleTest():
numActions = 10
numRounds = 10000
biases = [1.0 / k for k in range(2, 12)]
rewardVector = [[1 if random.random() < bias else 0 for bias in biases]
for _ in range(numRounds)]
rewards = lambda choice, t: rewardVector[t][choice]
bestAction = max(range(numActions),
key=lambda action: sum(
[rewardVector[t][action] for t in range(numRounds)]))
bestUpperBoundEstimate = 2 * numRounds / 3
gamma = math.sqrt(numActions * math.log(numActions) /
((math.e - 1) * bestUpperBoundEstimate))
gamma = 0.07
cumulativeReward = 0
bestActionCumulativeReward = 0
weakRegret = 0
t = 0
for (choice, reward, est, weights) in exp3(numActions, rewards, gamma):
cumulativeReward += reward
bestActionCumulativeReward += rewardVector[t][bestAction]
weakRegret = (bestActionCumulativeReward - cumulativeReward)
regretBound = (math.e - 1) * gamma * bestActionCumulativeReward + (
numActions * math.log(numActions)) / gamma
print("regret: %d\tmaxRegret: %.2f\tweights: (%s)" %
(weakRegret, regretBound, ', '.join(
["%.3f" % weight for weight in distr(weights)])))
t += 1
if t >= numRounds:
break
print(cumulativeReward)
def simpleTest():
numActions = 10
numRounds = 10000
biases = [1.0 / k for k in range(2,12)]
rewardVector = [[1 if random.random() < bias else 0 for bias in biases] for _ in range(numRounds)]
rewards = lambda choice, t: rewardVector[t][choice]
bestAction = max(range(numActions), key=lambda action: sum([rewardVector[t][action] for t in range(numRounds)]))
bestUpperBoundEstimate = 2 * numRounds / 3
gamma = math.sqrt(numActions * math.log(numActions) / ((math.e - 1) * bestUpperBoundEstimate))
gamma = 0.07
cumulativeReward = 0
bestActionCumulativeReward = 0
weakRegret = 0
t = 0
for (choice, reward, est, weights) in exp3(numActions, rewards, gamma):
cumulativeReward += reward
bestActionCumulativeReward += rewardVector[t][bestAction]
weakRegret = (bestActionCumulativeReward - cumulativeReward)
regretBound = (math.e - 1) * gamma * bestActionCumulativeReward + (numActions * math.log(numActions)) / gamma
print("regret: %d\tmaxRegret: %.2f\tweights: (%s)" % (weakRegret, regretBound, ', '.join(["%.3f" % weight for weight in distr(weights)])))
t += 1
if t >= numRounds:
break
print(cumulativeReward)
