def findPathToClosestDot(self, gameState: pacman.GameState):
"""
Returns a path (a list of actions) to the closest dot, starting from
gameState.
"""
# Here are some useful elements of the startState
startPosition = gameState.getPacmanPosition()
food = gameState.getFood()
walls = gameState.getWalls()
problem = AnyFoodSearchProblem(gameState)
# we don't know where the closest dot is, so let's estimate the closest dot
import time
start_time = time.time()
# print("food (%s): %s" %(type(food), food))
# getting the closest dot to set it as the goal
problem.goal = __getClosestGoal__(
startPosition,
food.asList()) # so that the heuristic knows the goal
import search
astar = search.astar(problem, heuristic=euclideanHeuristic)
print("findPathToClosestDot() took %2.5f seconds" %
(time.time() - start_time))
return astar
def betterEvaluationFunction(currentGameState: GameState):
"""
Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
evaluation function (question 5).
DESCRIPTION: <write something here so we know what you did>
"""
"*** YOUR CODE HERE ***"
pos = currentGameState.getPacmanPosition()
foods = currentGameState.getFood()
walls = currentGameState.getWalls()
capsules = currentGameState.getCapsules()
ghosts = currentGameState.getGhostStates()
score = currentGameState.getScore()
foods_cost = _food_cost(foods.asList(), pos, walls)
capsules_cost = _food_cost(capsules, pos, walls)
ghosts_dis, s_ghosts_dis = _ghost_cost(ghosts, pos, walls)
def d(x):
if x == 0:
return float('inf')
return 9 / (x**2)
ghosts_cost = sum(map(d, ghosts_dis))
s_ghosts_cost = sum(map(lambda x: x[0], filter(lambda x: x[0] < x[1], s_ghosts_dis)))
return score - (2 * foods_cost) - capsules_cost - s_ghosts_cost - ghosts_cost
def __init__(self, startingGameState: pacman.GameState):
self.start = (startingGameState.getPacmanPosition(),
startingGameState.getFood())
self.walls = startingGameState.getWalls()
self.startingGameState = startingGameState
self._expanded = 0 # DO NOT CHANGE
self.heuristicInfo = {
} # A dictionary for the heuristic to store information
def __init__(self, startingGameState: pacman.GameState):
"""
Stores the walls, pacman's starting position and corners.
"""
self.walls = startingGameState.getWalls()
self.startingPosition = startingGameState.getPacmanPosition()
top, right = self.walls.height - 2, self.walls.width - 2
self.corners = ((1, 1), (1, top), (right, 1), (right, top))
for corner in self.corners:
if not startingGameState.hasFood(*corner):
print('Warning: no food in corner ' + str(corner))
self._expanded = 0 # DO NOT CHANGE; Number of search nodes expanded
# Please add any code here which you would like to use
# in initializing the problem
"*** YOUR CODE HERE ***"
self.startState = PositionWithFoods(self.startingPosition,
self.corners)
self.to_foods: Dict[Coordinate, Dict[Coordinate, float]] = {}
for c in self.corners:
queue = util.Queue()
queue.push((c, 0))
distances = {}
while not queue.isEmpty():
pos, dis = queue.pop()
x, y = pos
if pos in distances:
continue
distances[pos] = dis
for action in DIRECTIONS:
dx, dy = Actions.directionToVector(action)
next_pos = int(x + dx), int(y + dy)
nextx, nexty = next_pos
if next_pos not in distances and not self.walls[nextx][
nexty]:
queue.push((next_pos, dis + 1))
self.to_foods[c] = distances
self.to_other_foods = {}
def tmp(foods, curr):
key = (curr, tuple(sorted(foods)))
if len(foods) == 1:
self.to_other_foods[key] = 0
return 0
if key in self.to_other_foods:
return self.to_other_foods[key]
left_overs = list(foods)
left_overs.remove(curr)
self.to_other_foods[key] = min(
map(lambda c: self.to_foods[curr][c] + tmp(left_overs, c),
left_overs))
return self.to_other_foods[key]
for c in self.corners:
tmp(self.corners, c)
def __init__(self, gameState: pacman.GameState):
"Stores information from the gameState. You don't need to change this."
# Store the food for later reference
self.food = gameState.getFood()
# Store info for the PositionSearchProblem (no need to change this)
self.walls = gameState.getWalls()
self.startState = gameState.getPacmanPosition()
self.costFn = lambda x: 1
self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE
def __init__(self, start_game_state: GameState):
super().__init__(start_game_state)
self._expanded = 0 # DO NOT CHANGE; Number of search nodes expanded
self.startingPosition = start_game_state.getPacmanPosition()
self.capsules = tuple(start_game_state.getCapsules())
self.foods = start_game_state.getFood()
self.walls = start_game_state.getWalls()
self.costFn = lambda x: 1
self.start_game_state = start_game_state
self.is_eating_capsule = True
def findPathToClosestDot(self, gameState: pacman.GameState):
"""
Returns a path (a list of actions) to the closest dot, starting from
gameState.
"""
# Here are some useful elements of the startState
startPosition = gameState.getPacmanPosition()
food = gameState.getFood()
walls = gameState.getWalls()
problem = AnyFoodSearchProblem(gameState)
"*** YOUR CODE HERE ***"
return search.bfs(problem)
def mazeDistance(point1, point2, gameState: pacman.GameState):
"""
Returns the maze distance between any two points, using the search functions
you have already built. The gameState can be any game state -- Pacman's
position in that state is ignored.
Example usage: mazeDistance( (2,4), (5,6), gameState)
This might be a useful helper function for your ApproximateSearchAgent.
"""
x1, y1 = point1
x2, y2 = point2
walls = gameState.getWalls()
assert not walls[x1][y1], 'point1 is a wall: ' + str(point1)
assert not walls[x2][y2], 'point2 is a wall: ' + str(point2)
prob = PositionSearchProblem(gameState,
start=point1,
goal=point2,
warn=False,
visualize=False)
return len(search.bfs(prob))
