def repDynamique(initStates, goalStates, wallStates):
# Initialisation
nPlayers = len(initStates)
nObjectives = len(goalStates)
sObjectives = [0 for i in range(nObjectives)]
aPath = [range(nPlayers) for i in range(nObjectives)]
# Generation des chemins
for i in range(nPlayers):
for j in range(nObjectives):
aPath[i][j] = doAStar(initStates[i], goalStates[j], wallStates)
aPath[i].sort(key=lambda a: len(a))
# Encheres
while sObjectives.count(0) > 0:
for i in nPlayers:
if sObjectives[i] >= nObjectives:
print "boucle infinie"
return repEnchere(initStates, goalStates, wallStates)
#raise Exception('Echec de la resolution')
if sObjectives[i] >= 0 and sObjectives[i] < nObjectives:
o = aPath[sObjectives[i] % 2]
if sObjectives.count(o) == 0:
sObjectives[i] = o
else:
sObjectives[i] += 1
sObjectives.remove(o)
def repEnchere(initStates, goalStates, wallStates):
nbPlayers = len(initStates)
nbObj = len(goalStates)
path = range(nbPlayers)
playerPath = [range(nbPlayers) for i in range(nbObj)]
pathSorter = []
for i in range(nbObj):
for j in range(nbPlayers):
playerPath[i][j] = doAStar(initStates[j], goalStates[i],
wallStates)
heapq.heappush(pathSorter, (len(playerPath[i][j]), (i, j)))
cpt = 0
iList = []
jList = []
while cpt < nbPlayers:
act = heapq.heappop(pathSorter)
i, j = act[1]
if (iList.count(i) == 0 and jList.count(j) == 0):
path[j] = playerPath[i][j]
iList.append(i)
jList.append(j)
cpt += 1
#print(path)
return path
def repDynamique(initStates, goalStates, wallStates):
# Initialisation
nPlayers = len(initStates)
nObjectives = len(goalStates)
sObjectives = [0 for i in range(nObjectives)]
aPath = [range(nPlayers) for i in range(nObjectives)]
# Generation des chemins
for i in range(nPlayers):
for j in range(nObjectives):
aPath[i][j] = doAStar(initStates[i], goalStates[j], wallStates)
aPath[i].sort(key = lambda a: len(a))
# Encheres
while sObjectives.count(0) > 0:
for i in nPlayers:
if sObjectives[i] >= nObjectives:
print "boucle infinie"
return repEnchere(initStates, goalStates, wallStates)
#raise Exception('Echec de la resolution')
if sObjectives[i] >= 0 and sObjectives[i] < nObjectives:
o = aPath[sObjectives[i]%2]
if sObjectives.count(o) == 0:
sObjectives[i] = o
else:
sObjectives[i]+= 1
sObjectives.remove(o)
def repEnchere(initStates, goalStates, wallStates): nbPlayers = len(initStates) nbObj = len(goalStates) path = range(nbPlayers) playerPath = [range(nbPlayers) for i in range(nbObj)] pathSorter = [] for i in range(nbObj): for j in range(nbPlayers): playerPath[i][j] = doAStar(initStates[j], goalStates[i], wallStates) heapq.heappush(pathSorter, (len(playerPath[i][j]), (i, j))) cpt = 0 iList = [] jList = [] while cpt < nbPlayers: act = heapq.heappop(pathSorter) i,j = act[1] if(iList.count(i) == 0 and jList.count(j) == 0): path[j] = playerPath[i][j] iList.append(i) jList.append(j) cpt += 1 #print(path) return path
def main():
init("pathfindingWorld_MultiPlayer2")
#-------------------------------
# Building the matrix
#-------------------------------
#initStates= []
# on localise tous les états initiaux (loc du joueur)
initStates = [o.get_rowcol() for o in game.layers['joueur']]
print ("Init states:", initStates)
# on localise tous les objets ramassables
goalStates = [o.get_rowcol() for o in game.layers['ramassable']]
print ("Goal states:", goalStates)
# on localise tous les murs
wallStates = [w.get_rowcol() for w in game.layers['obstacle']]
#print ("Wall states:", wallStates)
#-------------------------------
# Building the best path with A*
#-------------------------------
path = doAStar(initStates[0], goalStates[0], wallStates)
#-------------------------------
# Moving along the path
#-------------------------------
for i in path:
player.set_rowcol(i[0],i[1])
print ("pos:",i[0],i[1])
game.mainiteration()
# si on a trouvé l'objet on le ramasse
if (i[0],i[1])==goalStates[0]:
o = game.player.ramasse(game.layers)
game.mainiteration()
print ("Objet trouvé!")
break
'''
#x,y = game.player.get_pos()
'''
pygame.quit()
def repGaleShapley(initStates, goalStates, wallStates):
# Initialisation
nPlayers = len(initStates)
nObjectives = len(goalStates)
allPath = [range(nPlayers) for i in range(nObjectives)]
pPath = []
pPathGS = []
oPath = []
oPathGS = []
# Generation des chemins
for i in range(nPlayers):
for j in range(nObjectives):
allPath[i][j] = doAStar(initStates[i], goalStates[j], wallStates)
pPath.append(range(nObjectives))
pPath[i].sort(key = lambda a : len(allPath[i][a]))
pPathGS.append([i] + pPath[i])
# print("PATH INDEX OBJ : ", goalStates.index(pPath[i][j][-1]))
# Choix pour les objectifs
for i in range(nObjectives):
oPath.append(range(nPlayers))
oPath[i].sort(key = lambda a : len(allPath[a][i]))
oPathGS.append([i] + oPath[i])
# Gale-Shapley
matcher = PartyMatcher()
matcher.setApplicants(oPathGS)
matcher.setProviders(pPathGS)
matcher.match()
matches = matcher.getMatch()
# Application
path = range(nPlayers)
for i in range(len(matches)):
#print("MATCH : ", matches[i][1])
#print("PATH : ", pPath[i][pPathGS[i].index(matches[i][1])])
x,y = matches[i]
path[x] = allPath[x][y]
#path[i] = pPath[i][pPathGS[i].index()]
return path
def repGaleShapley(initStates, goalStates, wallStates):
# Initialisation
nPlayers = len(initStates)
nObjectives = len(goalStates)
allPath = [range(nPlayers) for i in range(nObjectives)]
pPath = []
pPathGS = []
oPath = []
oPathGS = []
# Generation des chemins
for i in range(nPlayers):
for j in range(nObjectives):
allPath[i][j] = doAStar(initStates[i], goalStates[j], wallStates)
pPath.append(range(nObjectives))
pPath[i].sort(key=lambda a: len(allPath[i][a]))
pPathGS.append([i] + pPath[i])
# print("PATH INDEX OBJ : ", goalStates.index(pPath[i][j][-1]))
# Choix pour les objectifs
for i in range(nObjectives):
oPath.append(range(nPlayers))
oPath[i].sort(key=lambda a: len(allPath[a][i]))
oPathGS.append([i] + oPath[i])
# Gale-Shapley
matcher = PartyMatcher()
matcher.setApplicants(oPathGS)
matcher.setProviders(pPathGS)
matcher.match()
matches = matcher.getMatch()
# Application
path = range(nPlayers)
for i in range(len(matches)):
#print("MATCH : ", matches[i][1])
#print("PATH : ", pPath[i][pPathGS[i].index(matches[i][1])])
x, y = matches[i]
path[x] = allPath[x][y]
#path[i] = pPath[i][pPathGS[i].index()]
return path
return state
parser = argparse.ArgumentParser()
parser.add_argument("problem_file")
parser.add_argument("init_solution_file")
parser.add_argument("num_trucks")
parser.add_argument("truck_capacity")
parser.add_argument("world_record_score")
parser.add_argument("--plot", help="plot the map before the main loop engages", action = "store_true")
parser.add_argument("--test", help="looks into the test directory rather than main directory", action = "store_true")
args = parser.parse_args()
problem_file = args.problem_file
init_solution_file = args.init_solution_file
num_trucks = int(args.num_trucks)
do_plot = args.plot
truck_capacity = int(args.truck_capacity)
world_record_score = float( args.world_record_score )
test_environment = args.test
init(problem_file)
state = doAStar(initial_state(problem_file, init_solution_file), do_plot, world_record_score)
states = []
while state.parent != None:
# raw_input("\nEnter to see parent")
# state.plot()
states.append(state)
state = state.parent
filename = "solution_path_" + datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
write_solution_path(states, filename)
