def apply_monte_carlo_simulation( costOfLiving , numTimes ):
utilMap = UtilityMap()
transitionModel = TransitionModel()
rewardSet = RewardSet( costOfLiving )
utilMap = Problem1.apply_value_iteration( utilMap , transitionModel , rewardSet )
policy = utilMap.get_optimal_policy( transitionModel )
rewards = []
for _ in range( numTimes ):
rewards.append( simulate_run( 2 , 3 , policy , rewardSet ) )
return rewards
def apply_monte_carlo_simulation(costOfLiving, numTimes):
utilMap = UtilityMap()
transitionModel = TransitionModel()
rewardSet = RewardSet(costOfLiving)
utilMap = Problem1.apply_value_iteration(utilMap, transitionModel,
rewardSet)
policy = utilMap.get_optimal_policy(transitionModel)
rewards = []
for _ in range(numTimes):
rewards.append(simulate_run(2, 3, policy, rewardSet))
return rewards
def solve():
utilMap = UtilityMap()
transitionModel = TransitionModel()
rewardSet = RewardSet( -0.04 )
utilMap = Problem1.apply_value_iteration( utilMap , transitionModel , rewardSet )
rewards10 = apply_monte_carlo_simulation( -0.04 , 10 )
print "10 run mean:", sum( rewards10 ) / 10.0
print "10 run stddev:" , numpy.std( numpy.array( rewards10 ) )
rewards100 = apply_monte_carlo_simulation( -0.04 , 100 )
print "100 run mean:" , sum( rewards100 ) / 100.0
print "100 run stddev:" , numpy.std( numpy.array( rewards100 ) )
rewards1000 = apply_monte_carlo_simulation( -0.04 , 1000 )
print "1000 run mean:" , sum( rewards1000 ) / 1000.0
print "1000 run stddev:" , numpy.std( numpy.array( rewards1000 ) )
return (utilMap , rewards10 , rewards100 , rewards1000)
def solve():
utilMap = UtilityMap()
transitionModel = TransitionModel()
rewardSet = RewardSet(-0.04)
utilMap = Problem1.apply_value_iteration(utilMap, transitionModel,
rewardSet)
rewards10 = apply_monte_carlo_simulation(-0.04, 10)
print "10 run mean:", sum(rewards10) / 10.0
print "10 run stddev:", numpy.std(numpy.array(rewards10))
rewards100 = apply_monte_carlo_simulation(-0.04, 100)
print "100 run mean:", sum(rewards100) / 100.0
print "100 run stddev:", numpy.std(numpy.array(rewards100))
rewards1000 = apply_monte_carlo_simulation(-0.04, 1000)
print "1000 run mean:", sum(rewards1000) / 1000.0
print "1000 run stddev:", numpy.std(numpy.array(rewards1000))
return (utilMap, rewards10, rewards100, rewards1000)
