def followActionBasis(self, observedState, action):
allowedDirections = observedState.getLegalPacmanActions()
pacmanPosition = observedState.getPacmanPosition()
if action == 'good':
target = classifyGhosts.getNearestGoodGhost(observedState, self.distancer)
elif action == 'bad':
target = classifyGhosts.getNearestBadGhost(observedState, self.distancer)
elif action == 'capsule':
target = classifyCapsule.closest(observedState, self.distancer)
else:
raise Exception("Unknown action '%s' passed to action basis" % action)
# find the direction that moves closes to target
best_action = Directions.STOP
best_dist = np.inf
for la in allowedDirections:
if la == Directions.STOP:
continue
successor_pos = Actions.getSuccessor(pacmanPosition,la)
new_dist = self.distancer.getDistance(successor_pos,target)
if new_dist < best_dist:
best_action = la
best_dist = new_dist
return best_action
def ghostPosition(self,observedState): pacmanPosition = observedState.getPacmanPosition() ghost_positions = [ classifyGhosts.getNearestGoodGhost(observedState, self.distancer), classifyGhosts.getNearestBadGhost(observedState, self.distancer)] bins = 5 xBinner = xbinner( (0,observedState.layout.width), bins ) yBinner = xbinner( (0,observedState.layout.height), bins ) state = (xBinner(pacmanPosition[0]), yBinner(pacmanPosition[1])) for pos in ghost_positions: state += (xBinner(pos[0]),yBinner(pos[1])) return state
def goodBadGhostCapsuleDistances(self,observedState): pacmanPosition = observedState.getPacmanPosition() goodGhost = classifyGhosts.getNearestGoodGhost(observedState, self.distancer) badGhost = classifyGhosts.getNearestBadGhost(observedState, self.distancer) capsule = classifyCapsule.closest(observedState, self.distancer) distBinner = xbinner( \ (0,math.sqrt(observedState.layout.width**2 + observedState.layout.width**2)), \ goodBadGhostCapsuleDistances.buckets ) goodGhostDistance = distBinner(self.distancer.getDistance(pacmanPosition, goodGhost)) badGhostDistance = distBinner(self.distancer.getDistance(pacmanPosition, badGhost)) capsuleDistance = distBinner(self.distancer.getDistance(pacmanPosition, capsule)) hasCapsule = observedState.scaredGhostPresent() return (goodGhostDistance,badGhostDistance,capsuleDistance,int(hasCapsule))
def localNeighborhood(self, observedState): radius = 4 pacmanPosition = observedState.getPacmanPosition() xBinner = xbinner( (pacmanPosition[0]-radius, pacmanPosition[0]+radius), localNeighborhood.buckets ) yBinner = xbinner( (pacmanPosition[1]-radius, pacmanPosition[1]+radius), localNeighborhood.buckets ) goodGhost = classifyGhosts.getNearestGoodGhost(observedState, self.distancer) badGhost = classifyGhosts.getNearestBadGhost(observedState, self.distancer) capsule = classifyCapsule.closest(observedState, self.distancer) hasCapsule = observedState.scaredGhostPresent() print pacmanPosition, goodGhost, badGhost, capsule, hasCapsule # bin the x/y position for each object of interest b = [] for p in [goodGhost, badGhost, capsule]: b += [xBinner(p[0]), yBinner(p[1])] return tuple(b + [int(hasCapsule)])