def train(number_of_games=100, search_depth=2):
assert search_depth % 2 == 0
weights = random_weights(len(heuristic.fs))
weights[len(heuristic.fs)-1] = 1
weights[len(heuristic.fs)-2] = 1
# print "Weights for the players: %s" % weights
c1 = ComputerPlayer("muzi", weights, search_depth = 2, learning_rate=0.09)
c2 = ComputerPlayer("thanh", copy.deepcopy(weights), search_depth = 2, learning_rate=0.04)
players = {"muzi": c1, "thanh":c2}
score_status = [0,0,0]
for n in range(number_of_games):
# print "Game %i" % n
score_buffer = []
game = twixt.Twixt(c1.name, c2.name, (6,6))
game.id = str(n)
game.claim_node((int(round(random()*(game.size[0]-3)+1)),int(round(random()*(game.size[0]-3)+1))), c1.name)
trainee, teacher = c1, c2
game.current_player = c2.name
while True:
if not heuristic.get_valid_nodes(game):
# print "Nobody won..."
score_status[2] += 1
break
## print "asking %s for his move..." % game.current_player
xy = players[game.current_player].next_minmax_move(game)
try:
game.claim_node(xy, game.current_player)
except twixt.NodeError, e:
# print "%s reserved for %s" % (xy, game.nodes[xy].reservee)
raise e
# print game.current_player + " claimed node %s." % str(xy)
if trainee.name == game.current_player and len(score_buffer) == search_depth/2:
# print "Updating trainee..."
expected_score = score_buffer.pop(0)
actual_score = trainee.get_score(game)
score_buffer.append(actual_score)
effs = [f_i(game, trainee.name) for f_i in heuristic.fs]
old_weights = copy.deepcopy(trainee.weights)
trainee.update_weights(expected_score, actual_score, effs)
# print [old_weights[i] - trainee.weights[i] for i in range(len(trainee.weights))]
elif trainee.name == game.current_player and len(score_buffer) != search_depth/2:
actual_score = trainee.get_score(game)
score_buffer.append(actual_score)
if game.has_won(game.current_player):
#print "%s HAS WON THE GAME!" % game.current_player
if game.current_player == "muzi":
score_status[0] +=1
else:
score_status[1] +=1
break
else:
game.current_player = game.opponent(game.current_player)
#render_game_board_image(game)
print "Game %s finished: Game status [Muzi,Thanh,Draw] = %s" % (n,score_status)
if n%10 == 0:
print "Trained weights: %s" % c1.weights
del game
def train(number_of_games=100, search_depth=2):
assert search_depth % 2 == 0
# The following weights are tuned for (unscaled) 0.5 * g_1 plus output of perceptron with f_1, ..., f_5.
# weights = [0.80645065474968314, 0.73851632090876906, -0.018709876472095509, 0.067931480573086053, 0.341251139029706]
# weights = [0.78256345518575787, 0.72806061248232179, -0.047892301965649302, -0.0088772339336371277, 0.34506233417550852]
# weights = [0.77115418840969974, 0.71555104743300879, -0.00081140756190061988, -0.054876323944738327, 0.3665955789374965]
# weights = [0.74288563817636388, 0.70486729316165075, 0.052725832294392469, -0.098542258526105236, 0.44418131498229901]
# These weights are due to 100 generations of the player with [f_1, f_2, ..., f_5] and g_1 (scaled.)
# weights = [0.8098611843515332, 0.74123044895203627, -0.039245974600852558, 0.11163072341922184, 0.35739214093828314]
random.seed(time.time())
effs = [heuristic.f_1,heuristic.f_2,heuristic.f_4,heuristic.f_5, heuristic.f_8, heuristic.f_9, heuristic.f_10]
weights = random_weights(len(effs))
c1 = PerceptronComputerPlayer("muzi", effs=effs, g = heuristic.g_1, weights=weights, search_depth = 2, learning_rate=0.05)
c2 = PerceptronComputerPlayer("thanh", effs=[], g=heuristic.g_1, weights=[], search_depth = 2, learning_rate=0.04)
players = {"muzi": c1, "thanh":c2}
score_status = [0,0,0]
for n in range(number_of_games):
score_buffer = []
game = twixt.Twixt(c1.name, c2.name, (6,6))
game.id = str(n)
game.claim_node((2,3), c1.name)
game.claim_node((3,2), c2.name)
# trainee, teacher = c1, c2
trainee, teacher = Dummy(""), Dummy("")
game.current_player = c1.name
while True:
if not heuristic.get_valid_nodes(game):
# print "Nobody won..."
score_status[2] += 1
break
## print "asking %s for his move..." % game.current_player
xy = players[game.current_player].next_minmax_move(game)
try:
game.claim_node(xy, game.current_player)
except twixt.NodeError, e:
# print "%s reserved for %s" % (xy, game.nodes[xy].reservee)
raise e
# print game.current_player + " claimed node %s." % str(xy)
if trainee.name == game.current_player and len(score_buffer) == search_depth/2:
# print "Updating trainee..."
expected_score = score_buffer.pop(0)
actual_score = trainee.get_score(game)
score_buffer.append(actual_score)
effs = [f_i(game, trainee.name) for f_i in trainee.effs]
#old_weights = copy.deepcopy(trainee.weights)
error = trainee.update_weights(expected_score, actual_score, effs)
print "error: %r" % error
# print [old_weights[i] - trainee.weights[i] for i in range(len(trainee.weights))]
elif trainee.name == game.current_player and len(score_buffer) != search_depth/2:
actual_score = trainee.get_score(game)
score_buffer.append(actual_score)
if game.has_won(game.current_player):
#print "%s HAS WON THE GAME!" % game.current_player
if game.current_player == "muzi":
score_status[0] +=1
else:
score_status[1] +=1
break
else:
game.current_player = game.opponent(game.current_player)
render_game_board_image(game)
print "Game %s finished: Game status [Muzi,Thanh,Draw] = %s" % (n,score_status)
if n%5 == 0:
print "Trained weights: %s" % c1.weights
del game
