def TestSim(obs):
MaxY = 16
MaxX = 22
state = WorldState(obs)
print "mario loc ", state.mario.x, " ", state.mario.y
commonVar = getCommonVar()
classVarList = getClassVar()
rewardVar = orange.FloatVariable("reward")
RewardLearner = Learner(commonVar, [rewardVar], 3000)
commonVar.pop(0)
DynamicLearner = Learner(commonVar, classVarList, 3000)
lastActionId = 9
modelFea = getModelFeature(state, [2.0, 1.0, 0.0, 0.0])
rewardFea = getTrainFeature(state, [0.0], lastActionId) # don't learn the pseudo reward
DynamicLearner.add([modelFea])
RewardLearner.add([rewardFea])
dynaLearner = [DynamicLearner for action in range(12)]
path = Optimize(state, dynaLearner, RewardLearner, 100, [], ActionRange)
newState = ExpandPath(path, MakeSimState(state, 10), dynaLearner, RewardLearner)
print type(newState)
print "hello"
for world in newState.worldList:
print "loc: ", world.mario.x
def initLearner(self):
if self.DynamicLearner == {}:
commonVar = getCommonVar()
classVarList = getClassVar()
#rewardVar = orange.FloatVariable("reward")
#self.RewardLearner = Learner(commonVar, [rewardVar], 6000)
commonVar.pop(0)
for action in self.actionList:
if action == 9:
maxFeature = 10000
else:
maxFeature = 3000
self.DynamicLearner[action] = Learner(commonVar, classVarList, maxFeature)
