def highScoreEvaluationFunction(current):
"""
* if there are any capsules left, move towards them
* if we are out of capsules, gather remaining food
* Try _really_ hard to get a scared ghost if we can
* if there is a chance of death, don't risk it
"""
pacman = current.getPacmanPosition()
foods = current.getFood()
walls = current.getWalls()
ghosts = current.getGhostStates()
distance = util.manhattanDistance
capsules = current.getCapsules()
score = current.getScore()
if len(capsules) > 0:
score += 5 * (1.0 / any_capsule_find(current))
else: score += 1 * (1.0 / any_food_find(current))
max_scared = 0
for ghost in ghosts:
gp = ghost.getPosition()
gt = ghost.scaredTimer
if gt > 0:
distance = specific_find(current, pacman, gp)
if gt >= distance:
value = 200 * (1.0 / distance)
max_scared = max(max_scared, value)
elif pacman == gp: score = -1000
return score + max_scared
def betterEvaluationFunction(current):
"""
* if there are any capsules left, lets really move towards
the closest one.
* If there is food left, try to move to the closest
* For each ghost
- if they are scared and nearby, chase them
- otherwise never be in the same position as them
"""
pacman = current.getPacmanPosition()
foods = current.getFood()
walls = current.getWalls()
ghosts = current.getGhostStates()
distance = util.manhattanDistance
capsules = current.getCapsules()
score = current.getScore()
if len(capsules) > 0:
mcaps = min(specific_find(current, pacman, c) for c in capsules)
score += 20 if mcaps == 0 else 5 * math.ceil(1.0 / mcaps)
# if foods.count() > 0:
# dfood = [distance(f, pacman) for f in foods.asList()]
# mfood = min(dfood)
# cfood = sum(1.0 for f in dfood if f == mfood)
# score += 1.0 / mfood
score += 1.0 / any_food_find(current)
for ghost in ghosts:
gp = ghost.getPosition()
gd = distance(gp, pacman)
gt = ghost.scaredTimer
if gt != 0 and gd <= gt: score += 200
elif pacman == gp: score -= 500
return score