def calCommitStep(initGrid, target1, target2, trajectory):
initGrid = trajectory[0]
distanceDiff = abs(calculateGridDis(initGrid, target1) - calculateGridDis(initGrid, target2))
minDisToTarget = min([calculateGridDis(selfGrid, target) for target in[target1, target2]])
currentSteps = trajectory.index(selfGrid)
return commitStep
def __call__(self, selfGrid, target1, target2, trajectory):
initGrid = trajectory[0]
goal = trajectory[-1]
midpoint = calMidPoints(selfGrid, target1, target2)
zone = calculateAvoidCommitmnetZoneAll(initGrid, target1, target2)
if midpoint:
disToMidPoint = calculateGridDis(selfGrid, midpoint)
disToTargets = [calculateGridDis(selfGrid, target) for target in[target1, target2]]
isInDeliberationArea = 1 if disToMidPoint < min(disToTargets) else 0
if isInDeliberationArea:
actionDis = self.goalPolicy(selfGrid, midpoint)
else:
actionDis = self.goalPolicy(selfGrid, goal)
else:
actionDis = self.goalPolicy(selfGrid, goal)
return actionDis
def calDisToMidPointTrajLen(trajectory, target1, target2):
trajectory = list(map(tuple, trajectory))
playerGrid = trajectory[0]
midPoint = calMidPoints(playerGrid, target1, target2)
disToMidPointTraj = [
calculateGridDis(grid, midPoint) for grid in trajectory
]
meanDis = np.mean(disToMidPointTraj)
return meanDis
def calDisToMidPointTraj(trajectory, target1, target2):
trajectory = list(map(tuple, trajectory))
playerGrid = trajectory[0]
midPoint = calMidPoints(playerGrid, target1, target2)
disToMidPointTraj = [
calculateGridDis(grid, midPoint) for grid in trajectory
]
x = np.divide(np.arange(len(disToMidPointTraj)),
len(disToMidPointTraj) - 1)
y = np.array(disToMidPointTraj)
f = interp1d(x, y, kind='nearest')
xnew = np.linspace(0., 1., 30)
posterior = f(xnew)
return posterior
def isCommited(selfGrid, target1, target2):
distanceDiff = abs(
calculateGridDis(selfGrid, target1) -
calculateGridDis(selfGrid, target2))
pAvoidCommit = 1 / (distanceDiff + 1)
return 1 - pAvoidCommit