def __call__(self, state):
self.state = NodeState.SUCCESS
problem = sa.PositionSearchProblem(state, visualize=False)
pos = search.breadthFirstSearchPath(problem)
problem = sa.positionSearchP(state, goal=pos, visualize=False)
vals = search.aStarSearchAvoid(problem, state, sa.AvoidGhosts)
return vals[0]
def __call__(self, state):
gpos = state.getGhostPositions()
offsetx = 3
offsety = 2
walls = state.getWalls()
top, right = walls.height - 2, walls.width - 2
timer = []
for index in range(1, len(state.data.agentStates)):
timer.append(state.data.agentStates[index].scaredTimer)
if sum(timer) > 10/len(gpos):
vals = []
vals_len = []
for index in range(len(gpos)):
if timer[index] > 10:
tmppos = gpos[index]
x = int(tmppos[0])
y = int(tmppos[1])
newpos=(x,y)
if (x < right/2+offsetx and x > right/2-offsetx) and (y < top/2+offsety and y > top/2-offsety):
continue
problem = sa.positionSearchP(state, goal=newpos, visualize=False)
tmp_val = search.aStarSearchAvoid(problem,state,sa.AvoidGhosts)
vals.append(tmp_val)
vals_len.append(len(tmp_val))
self.state = NodeState.SUCCESS
if len(vals) > 0:
return vals[np.argmin(vals_len)][0]
self.state = NodeState.FAILED
return None
def __call__(self, state):
capsules = state.getCapsules()
## Set deppending on the level!
if (len(capsules) != 2):
self.firstEaten = True
else:
self.firstEaten = False
if not self.firstEaten:
vals = []
for capsule in capsules:
problem = sa.positionSearchP(state, goal=capsule)
vals.append(search.aStarSearch(problem, sa.manhattanHeuristic))
path = sorted(vals, key=len)[0]
self.state = NodeState.SUCCESS
return path[0]
else:
self.state = NodeState.FAILED
return None
def __call__(self, state):
capsules = state.getCapsules()
minammountCapsules = 2
## Set deppending on the level!
if (len(capsules) < minammountCapsules):
self.firstEaten = True
else:
self.firstEaten = False
if not self.firstEaten:
vals = []
for capsule in capsules:
problem = sa.positionSearchP(state, goal=capsule, visualize=False)
vals.append(search.aStarSearchAvoid(problem,state, sa.AvoidGhosts))
path = sorted(vals, key=len)[0]
self.state = NodeState.SUCCESS
return path[0]
else:
self.state = NodeState.FAILED
return None