name = 'frenzy_vs_betting.p'
start = time.time()
ALPHA = 0.001
LAMBS = [0.75]
n_train = []
callBot = calling_station.Calling_station()
bsbot = betting_station.Betting_station()
for LAMB in LAMBS:
net = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
auto = fw.AnotherAutoPlayer(net, name="superbot")
distance = 10
i = 0
while distance > 0.0002:
oldnet = auto.net.deepcopy()
auto.net.alpha /= 1.01
auto.train(1000, bsbot, debug=0, frenzy=1)
distance = UnbiasedNet.diff(auto.net, oldnet)
i = i + 1000
pickle.dump(auto, open(str(LAMB) + name, "wb"))
print "number of training:", i
n_train.append(i)
print "the training used", time.time() - start
opp_name = str(LAMB) + name
csBot = calling_station.Calling_station()
bsBot = betting_station.Betting_station()
for LAMB in LAMBS:
net = BiasedPerceptron(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
net2 = BiasedPerceptron(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
auto = fw.AnotherAutoPlayer(net, name="superbot")
ai = fw.AnotherAutoPlayer(net2, name='cpu', frenzy=1)
auto.train(n_train, bsBot, debug=0, frenzy=1)
pickle.dump(auto, open(str(LAMB) + name, "wb"))
print "the training used time", time.time() - start
j = 0
for LAMB in LAMBS:
auto = pickle.load(open(str(LAMB) + name, 'rb'))
result = []
for i in range(5):
result.append(auto.compete(bsBot, 2000, debug=0))
print 'Lambda:', LAMB
for LAMB in [0.6, 0.65, 0.7]:
name = ('specified_training_vs_Q' + str(num) + '_.005_' + str(LAMB) + '_' +
str(check_prob) + '_' + str(call_prob) + '_' + str(raise_prob) +
'_' + str(checkfold_prob) + '.p')
print n_cards
net1 = UnbiasedNet.NeuralNet(fw.n_in,
n_cards,
fw.n_out,
randomInit=True,
alpha=ALPHA,
lamb=LAMB,
subdiv=[(0, 0), (n_cards, n_cards),
(fw.n_in, n_cards)])
auto = fw.AnotherAutoPlayer(net1,
name="auto1",
check_prob=check_prob,
call_prob=call_prob,
raise_prob=raise_prob,
checkfold_prob=checkfold_prob)
ai = specified_prob.Specified_prob(
prob_list={
'Check': list_of_bots[num + 2][0],
'Call': list_of_bots[num + 2][1],
'Raise': list_of_bots[num + 2][2],
'CheckFold': list_of_bots[num + 2][3]
})
auto.train(n_train, ai, debug=0, frenzy=1)
pickle.dump(auto, open(name, "wb"))
result = []
for i in range(10):
result.append(auto.compete(ai, 5000, debug=0))
ALPHA = 0.005
n_train = 100000
list_of_bots= pickle.load(open("list_of_opponents.p", "rb"))
stat_obj = AnotherStatus()
n_cards = sum( len(stat_obj.vec_cards[key]) for key in stat_obj.vec_cards )
for LAMB in [0.6, 0.65]:
name = 'specified_trainning_vs_Q1_.005_' + str(LAMB) + '_1e5.p'
print n_cards
net1 = UnbiasedNet.NeuralNet(fw.n_in, n_cards, fw.n_out, randomInit=True,
alpha=ALPHA, lamb=LAMB,
subdiv=[(0,0), (n_cards,n_cards),
(fw.n_in,n_cards)])
auto = fw.AnotherAutoPlayer(net1, name="auto1", check_prob=0.3,
call_prob=0.3, raise_prob=0.1,
checkfold_prob=0.1)
ai = specified_prob.Specified_prob(prob_list={
'Check':list_of_bots[3][0], 'Call':list_of_bots[3][1],
'Raise':list_of_bots[3][2], 'CheckFold':list_of_bots[3][3]})
auto.train(n_train, ai, debug=0, frenzy=1)
pickle.dump(auto, open(name, "wb"))
result = []
for i in range(10):
result.append( auto.compete(ai, 5000, debug=0) )
data = '\n Learning rate: ' + str(ALPHA)
data += '\n Lambda: ' + str(LAMB)
data += '\n Number of training: ' + str(n_train)
data += '\n Results: ' + str(result)
print data
from tight_aggressive import Tight_aggressive
name = 'frenzy_vs_TA.p'
start = time.time()
ALPHA = 0.001
LAMBS = [0.9]
n_train = []
for LAMB in LAMBS:
net = UnbiasedNet.NeuralNet(another.n_in,
another.n_hidden,
another.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
auto = another.AnotherAutoPlayer(net, name="against_TA")
auto2 = Tight_aggressive()
distance = 10
i = 0
while distance > 0.0002:
oldnet = auto.net.deepcopy()
auto.net.alpha /= 1.01
auto.train(1000, auto2, debug=0, frenzy=1)
distance = UnbiasedNet.diff(auto.net, oldnet)
i = i + 1000
print "number of training:", i
pickle.dump(auto, open(str(LAMB) + name, "wb"))
n_train.append(i)
print "the training used time", time.time() - start
bsbot = betting_station.Betting_station()
next = self.status.check_first()
action = "Check"
self.status = next.copy()
#update the other guy's status vector resulting from your act
player2.status.vec_act[stage][1] = self.status.vec_act[stage][0]
player2.status.vec_act[stage][2] = self.status.vec_act[stage][2]
player2.status.stage = self.status.stage
return action
if __name__ == "__main__":
import pickle
# auto= pickle.load(open("player.p", "rb"))
# net= UnbiasedNet.NeuralNet(fw.n_in, fw.n_hidden, fw.n_out,
# alpha=0.001,
# lamb=0.5, randomInit=False)
auto = Tight_aggressive()
net = UnbiasedNet.NeuralNet(anotherStatus.n_in,
anotherStatus.n_hidden,
anotherStatus.n_out,
alpha=0.001,
lamb=0.9,
randomInit=True)
auto2 = anotherStatus.AnotherAutoPlayer(net, name="against_TA")
win = []
for i in range(10):
win.append(auto2.compete(auto, 2, debug=1))
print win
print np.mean(win)
print np.std(win)
<<<<<<< HEAD
for LAMB in [0.7, 0.65]:
=======
for LAMB in [0.6, 0.65]:
>>>>>>> a bunch of new bots. Try to get the new holdem file.
name = 'coarser_blind_vs_betting_.005_' + str(LAMB) + '_1e5.p'
print n_cards
net1 = UnbiasedNet.NeuralNet(fw.n_in, n_cards, fw.n_out, randomInit=True,
alpha=ALPHA, lamb=LAMB,
subdiv=[(0,0), (n_cards,n_cards),
(fw.n_in,n_cards)])
auto = fw.AnotherAutoPlayer(net1, name="auto1", check_prob=0.3,
<<<<<<< HEAD
call_prob=0.3, raise_prob=0.3, checkfold_prob=0.1)
=======
call_prob=0.3, raise_prob=0.1, checkfold_prob=0.03)
>>>>>>> a bunch of new bots. Try to get the new holdem file.
ai = Betting_station()
auto.train(n_train, ai, debug=0, frenzy=1)
pickle.dump(auto, open(name, "wb"))
result = []
for i in range(10):
result.append( auto.compete(ai, 5000, debug=0) )
data = '\n Learning rate: ' + str(ALPHA)
data += '\n Lambda: ' + str(LAMB)
data += '\n Number of training: ' + str(n_train)
