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This module contains different strategies for playing Pacman
import game
import pprint
from djikstra import shortestPath
from constants import *
class Strategy(object):
Base class for all strategies. Contains helper methods for
* creating graphs,
* finding ghosts,
* finding pacdots, etc.
def __init__(self, m, width): = m
self.width = width
self.pos = self.find_pacman_pos(
self.ghosts = self.find_ghosts_pos(
self.graph = None
def move_done(self, m, pos): = m
self.pos = pos
self.ghosts = self.find_ghosts_pos(
def find_pacman_pos(self, map):
for x, y, column in self.map_iter():
if column == PACMAN_TYPE:
return x, y
raise Exception("Pacman not found in map")
def find_ghosts_pos(self, map):
ghosts = []
for x, y, column in self.map_iter():
if column in GHOSTS:
ghosts.append((x, y))
return ghosts
def map_iter(self):
for x, row in enumerate(
for y, column in enumerate(row):
yield x, y, column
def make_graph(self):
Creates a dictionary holding the graph found on the grid.
A vertice(v) is (x, y) where x and y are the coordinates to that cell in the grid.
G[v] gives a dictionary where the key is the neighbour and the value is the cost of getting there(always 1).
graph = {}
for x, y, column in self.map_iter():
if not game.allowed_position(, (x, y)):
# left, up, right, down
edges = [(x, y-1), (x-1, y), (x, y+1), (x+1, y)]
# Remove any illegal positions
def f(p):
return 0 <= p[0] < self.width and 0 <= p[1] < self.width
edges = filter(f, edges)
# Remove any invalid positions, including positions with ghosts
edges = filter(lambda p: game.allowed_position(, p), edges)
graph[(x, y)] = dict([(p, 1) for p in edges])
return graph
def shortest_path(self, start, end):
#return brute_shortest_path(self.graph, start, end)
return shortestPath(self.graph, start, end)
def pos_to_direction(self, old, new):
Takes the old position, the new position, and translates it into
a string with the direction name that can be sent to the server.
# This could be improved..
if old[0] + 1 == new[0] and old[1] == new[1]:
return 'down'
elif old[1] + 1 == new[1] and old[0] == new[0]:
return 'right'
elif old[0] - 1 == new[0] and old[1] == new[1]:
return 'up'
elif old[1] - 1 == new[1] and old[0] == new[0]:
return 'left'
def is_food(self, pos):
return[pos[0]][pos[1]] in EATABLE_OBJECTS
def find_food(self, pos, maxdepth = 3):
Returns the coordinates of neighbours with food
Uses a breadth-first search, which will terminate when three
cells with food has been found.
food = []
q = [pos]
seen = []
while q:
current = q.pop(0)
for n in self.graph[current]:
if self.is_food(n):
if len(food) > 3:
if not n in seen:
return set(food)
class Random(Strategy):
class Dumb(Strategy):
class Suicidal(Strategy):
Heads strait for the closest ghost
class Hide(Strategy):
Tries to move as far away from the ghosts as possible
previous_cells = []
def move(self):
Returns the next move by inspecting the nearby tiles
This strategy is pretty simple:
Move towards the closest food time that takes Pacman the furthest away
from the ghosts.
# TODO: Detect when the ghosts will corner pacman in a dead-end
self.graph = self.make_graph()
#print shortestPath(self.graph, (0, 0), (4, 4))
ghosts = self.find_ghosts_pos(
# Find the closest food items, within a reasonable range
food = self.find_food(self.pos, maxdepth = 8)
#print 'food', list(food)
# Simulate taking the first step toward every food item
# Which step brings me the furthest away from the ghost?
next_steps = []
for food_position in food:
# TODO: This should ignore paths where there are ghosts
# Find shortest path to food. Remove the first step as it is
# the starting position.
path = self.shortest_path(self.pos, food_position)[1:]
distance = len(path)
# If Pacman were to take this step,
# what is the distance to the ghosts?
#distance_to_ghost = sum(map(lambda g: len(self.shortest_path(path[0], g)), ghosts))
distance_to_ghost = min(map(lambda g: len(self.shortest_path(path[0], g)), ghosts))
#print 'distance between', path[0], 'and', ghosts[0], 'is', distance_to_ghost
next_steps.append((distance, distance_to_ghost, path[0]))
def cmp(a, b):
# First sort by distance to food
diff = a[0] - b[0]
# If two items are equal, reverse sort by distance to ghost
return diff and diff or b[1] - a[1]
next_steps = sorted(next_steps, cmp = cmp)
# Discard steps where the distance is two or less to ghosts,
# as they are suicide
next_steps = filter(lambda (dist, g_dist, path): g_dist > 2, next_steps)
print next_steps
if next_steps:
next = next_steps[0][2]
# Check if we are stuck, if so, try to move away
if next in self.previous_cells[-2:]:
for distance, distance_to_ghost, n in next_steps:
if n not in self.previous_cells:
next = n
self.previous_cells = self.previous_cells[-5:]
return self.pos_to_direction(self.pos, next)
# No obvious next step because there is no food nearby
# Pick the direction that will take us the furthest away from
# the ghosts.
print 'no food nearby'
neighbours = []
for pos in self.graph[self.pos]:
path = self.shortest_path(ghosts[0], pos)
distance_to_ghost = min(map(lambda g: len(self.shortest_path(g, pos)), ghosts))
if path:
neighbours.append((distance_to_ghost - 1, pos))
neighbours = sorted(neighbours, reverse = True)
print neighbours
next = neighbours[0][1]
self.previous_cells = self.previous_cells[-5:]
return self.pos_to_direction(self.pos, next)
def brute_shortest_path(graph, start, end, path = []):
Returns the shortest path between start and end.
Implemented with a depth-first search.
path = path + [start]
if start == end:
return path
# If there are ghosts in the way, Pacman cannot go there, stupid
#if[start[0]][start[1]] in GHOSTS:
# return None
if not graph.has_key(start):
return None
shortest = None
for node in graph[start]:
if node not in path:
newpath = brute_shortest_path(graph, node, end, path)
if newpath:
if not shortest or len(newpath) < len(shortest):
shortest = newpath
return shortest