def smartPlays(game, tiles, player):
player /= 2
curr_game = game[player]
other_game = game[1 - player]
actions = curr_game.possible_actions(0)
if len(actions) == 1:
curr_game.update(actions[0][0])
other_game.update(actions[0][0])
print "I played a " + str(actions[0][0]) + ", yay!"
if not actions[0][0] == PASS_DOMINO:
tiles[2 * player].remove(actions[0][0])
else:
pnm = ProbabilisticNegaMax(curr_game)
depth = int(5 * (2**(1. / 3 * int(len(curr_game.dominos_played) / 4))))
print "DEPTH"
print depth
max_move, max_score = pnm.p_negamax_ab(depth, depth, -float("inf"),
float("inf"), 0)
# max_move, max_score = pnm.p_negamax(6,0)
curr_game.update(max_move[0], placement=max_move[1])
other_game.update(max_move[0], placement=max_move[1])
print "I played a " + str(max_move[0]) + ", yay!"
if not max_move[0] == PASS_DOMINO:
tiles[2 * player].remove(max_move[0])
return tiles
def smartPlays(game):
actions = game.possible_actions(0)
if len(actions) == 1:
game.update(actions[0][0])
print "I played a " + str(actions[0][0]) + ", yay!"
else:
pnm = ProbabilisticNegaMax(game)
max_move, max_score = pnm.p_negamax(5, 0)
game.update(max_move[0], placement=max_move[1])
print "I played a " + str(max_move[0]) + ", yay!"
def smartPlays(game, player):
player /= 2
curr_game = game[player]
other_game = game[1-player]
actions = curr_game.possible_actions(0)
if len(actions) == 1:
curr_game.update(actions[0][0])
other_game.update(actions[0][0])
print "I played a " + str(actions[0][0]) + ", yay!"
else:
pnm = ProbabilisticNegaMax(curr_game)
depth = int(6*(2**(1./3*int(len(curr_game.dominos_played)/4))))
print "DEPTH"
print depth
max_move, max_expectation = None, None
for a in actions:
curr_expectation = calculate_expectation(curr_game, depth, a)
if max_move is None or max_expectation < curr_expectation:
max_move, max_expectation = a, curr_expectation
print 'new max found with expectation : {}'.format(max_expectation)
# max_move, max_score = pnm.p_negamax(6,0)
curr_game.update(max_move[0], placement=max_move[1])
other_game.update(max_move[0], placement=max_move[1])
print "I played a " + str(max_move[0]) + ", yay!"
def smartPlays(game, tiles):
actions = game.possible_actions(1)
if len(actions) == 1:
game.update(actions[0][0])
# print "I played a " + str(actions[1][0]) + ", yay!"
else:
pnm = ProbabilisticNegaMax(game)
depth = int(5 * (2**(1. / 3 * int(len(game.dominos_played) / 4))))
print "DEPTH"
print depth
max_move, max_score = pnm.p_negamax_ab(depth, depth, -float("inf"),
float("inf"), 1)
# max_move, max_score = pnm.p_negamax(7,0)
game.update(max_move[1], placement=max_move[1])
# print "I played a " + str(max_move[1]) + ", yay!"
return tiles
def smartPlays(game, tiles, player):
player /= 2
curr_game = game[player]
other_game = game[1 - player]
actions = curr_game.possible_actions(0)
if len(actions) == 1:
curr_game.update(actions[0][0])
other_game.update(actions[0][0])
print "I played a " + str(actions[0][0]) + ", yay!"
if not actions[0][0] == PASS_DOMINO:
tiles[2 * player].remove(actions[0][0])
else:
pnm = ProbabilisticNegaMax(curr_game)
depth = int(5 * (2**(1. / 3 * int(len(curr_game.dominos_played) / 4))))
print "DEPTH"
print depth
# uncomment out the line below for oldSmartPlayer:
# max_move, max_score = pnm.p_negamax_ab(depth, depth, -float("inf"), float("inf"), 0)
# comment out up to after for loop for newSmartPlayer
max_move, max_expectation = None, None
for a in actions:
curr_expectation = calculate_expectation(curr_game, depth, a)
if max_move is None or max_expectation < curr_expectation:
max_move, max_expectation = a, curr_expectation
print 'new max found with expectation : {}'.format(
max_expectation)
# max_move, max_score = pnm.p_negamax(6,0)
curr_game.update(max_move[0], placement=max_move[1])
other_game.update(max_move[0], placement=max_move[1])
print "I played a " + str(max_move[0]) + ", yay!"
if not max_move[0] == PASS_DOMINO:
tiles[2 * player].remove(max_move[0])
return tiles
def newSmartPlays(game, tiles, player):
curr_game = game[player]
actions = curr_game.possible_actions(0)
if len(actions) == 1:
for g in games:
g.update(actions[0][0])
print "I played a " + str(actions[0][0]) + ", yay!"
if not actions[0][0] == PASS_DOMINO:
tiles[player].remove(actions[0][0])
else:
pnm = ProbabilisticNegaMax(curr_game)
depth = int(5*(2**(1./3*int(len(curr_game.dominos_played)/4))))
print "DEPTH"
print depth
max_move, max_expectation = None, None
for a in actions:
curr_expectation = calculate_expectation(curr_game, depth, a)
if max_move is None or max_expectation < curr_expectation:
max_move, max_expectation = a, curr_expectation
print 'new max found with expectation : {}'.format(max_expectation)
# max_move, max_score = pnm.p_negamax(6,0)
for g in games:
g.update(max_move[0], placement=max_move[1])
print "I played a " + str(max_move[0]) + ", yay!"
if not max_move[0] == PASS_DOMINO:
tiles[player].remove(max_move[0])
return tiles
def calculate_expectation(game, depth, move, samples=40):
exp_total = 0.0
remaining_dominoes = make_dominoes()
players = range(4)
pnm = ProbabilisticNegaMax(game)
game.make_probabilistic_move(0, move)
for t in game.dominos_played:
if not t == PASS_DOMINO:
remaining_dominoes.remove(t)
for _ in range(samples):
curr_dominoes = list(remaining_dominoes)
random.shuffle(curr_dominoes)
old_probabilities = deepcopy(game.probabilities)
while curr_dominoes:
curr_domino = curr_dominoes.pop()
curr_domino_probs = game.probabilities[curr_domino]
curr_assignment = choice(players, p=curr_domino_probs)
game._update_probs(curr_domino, curr_assignment)
exp_total += -pnm.p_negamax_ab(depth, depth, -float('inf'), float('inf'), 1)[1]
game.probabilities = old_probabilities
game.undo_move(0, move)
exp_total /= samples
return exp_total
'''
Just for simulating a game and checking that I am not a total mess
'''
game_tiles = []
for i in range(7):
for j in range(i, 7):
game_tiles.append((i, j))
my_tiles_input = ['12', '34', '22', '00', '55', '33', '23']
my_tiles = []
for t in my_tiles_input:
my_tiles.append(tuple([int(num) for num in t]))
starter = 3
start_tile = (6, 6)
test = Dominoes(game_tiles, my_tiles, starter, start_tile)
pnm = ProbabilisticNegaMax(test)
max_move, max_score = pnm.p_negamax(6, 0)
print max_move
# print test.curr_player
# c = deepcopy(test)
# prob = test.make_probabilistic_move(3, (PASS_DOMINO,None))
# print test.curr_player
# prob = test.make_probabilistic_move(0, (Domino(4, 6), None))
# print test.curr_player
# test.undo_move(0, (Domino(4,6), None))
# print test.curr_player
# test.undo_move(3, (PASS_DOMINO, None))
# print test.curr_player
# c.is_equal(test)