if debug:
print "Now bs call at stage ", stage
next = self.status.call()
action = "Call"
else:
next = self.status.praise()
if debug:
print "Now bs bet/raise at stage ", stage
action = "Raise"
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=0.02,
lamb=0.9,
randomInit=True)
auto = fw.Auto_player(net, name="superbot")
bs = Betting_station()
auto.train(10, bs, debug=1)
pickle.dump(auto, open("player.p", "wb"))
debug=debug)
game.endRound()
if debug:
print "End of one hand. The winning is", result[1], "\n"
start_cash += result[1]
return start_cash
if __name__ == "__main__":
ALPHA = 0.0005
LAMB = 0.6
n_train = 10
net1 = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
randomInit=True,
alpha=ALPHA,
lamb=LAMB)
auto1 = fw.Auto_player(net1, name="auto1", frenzy=True)
net2 = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
randomInit=True,
alpha=ALPHA,
lamb=LAMB)
auto2 = fw.Auto_player(net2, name="auto2", frenzy=True)
big_small = Big_small_blind(auto1, auto2, frenzy=True)
import calling_station
csbot = calling_station.Calling_station()
big_small.train(n_train, csbot, frenzy=True, debug=1)
big_small.compete(csbot)
import tight_aggressive
import framework
import numpy as np
import calling_station
csbot = calling_station.Calling_station()
preflop_net = UnbiasedNet.NeuralNet(framework.n_in,
framework.n_hidden,
framework.n_out,
alpha=0.001,
lamb=0.9,
randomInit=False,
momentum=0,
subdiv=[(0, 0), (52, framework.n_hidden),
(framework.n_in,
framework.n_hidden)])
preflop_bot = framework.Auto_player(preflop_net, name="preflop_bot")
bots = [preflop_bot]
for bot in bots:
bot.train(100000, csbot, frenzy=True)
pickle.dump(bot, open(bot.name + ".p", "wb"))
for bot in bots:
wins = []
for i in range(10):
wins.append(bot.compete(csbot, 1000, debug=0))
print "name= ", bot.name
print wins
print 'mean', np.mean(wins)
print 'std', np.std(wins)
if debug:
#print "after the decision is made at stage:", stage
#print self.status.vec_act[stage]
#print player2.status.vec_act[stage]
print self.name + " decided to ", game_actions[index]
return game_actions[index]
if __name__ == "__main__":
net = UnbiasedNet.NeuralNet(framework.n_in,
framework.n_hidden,
framework.n_out,
alpha=0.1,
lamb=0.9,
randomInit=True)
auto = framework.Auto_player(net, name="auto")
auto2 = Specified_prob(prob_list={
'Check': 1,
'Call': 1,
'Raise': 0.01,
'CheckFold': 0.01
})
auto.train(1, auto2, debug=1)
# auto.compete(auto2)
#create a raising bot, the number doesn't have to add up to be 1
auto3 = Specified_prob(prob_list={
'Check': 0.01,
'Call': 0.1,
'Raise': 0.99,
'CheckFold': 0.01