def chooseAction(self, gameState):
# First of all, we update the real distances
updateDistances(gameState)
move = KeyboardAgent.getAction(self, gameState)
printFile(move, gameState, 'training_keyboardAgent.txt')
global lastMove
lastMove = move
return move
def getAction( self, state):
action = KeyboardAgent.getAction(self, state);
# First time through, initialize the filter
if self.initialized == False:
self.initFilter(state)
self.initialized = True
# On all movement, step the filter and take an observation
if action != Directions.STOP:
print state.getLegalActions(self.index)
print state.getPacmanPosition()
self.moveStep()
self.observeAndResample(state)
self.computeWeightedMeanPosition()
return action
def chooseAction(self, gameState):
# First of all, we update the real distances
updateDistances(gameState)
t0 = time.clock()
move = KeyboardAgent.getAction(self, gameState)
t1 = time.clock() - t0
livingGhostsAndPacman = gameState.getLivingGhosts()
printStatistics(livingGhostsAndPacman, t1, 'statistics_keyboard.txt')
printFile(move, gameState, 'training_keyboard.arff', None)
global lastMove
global travelledDistance
lastMove = move
travelledDistance += 1
return move
def chooseAction(self, gameState): return KeyboardAgent.getAction(self, gameState)
def chooseAction(self, gameState):
action = KeyboardAgent.getAction(self, gameState)
self.prevAction = action
return action
def chooseAction(self, gameState):
return KeyboardAgent.getAction(self, gameState)
def chooseAction(self, gameState):
action = KeyboardAgent.getAction(self, gameState)
self.prevAction = action
return action
def chooseAction(self, gameState):
move = KeyboardAgent.getAction(self, gameState)
self.f.write(BustersAgent.printLineData(self, gameState, move))
return move
