name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name,
reg=r,
bankroll=10000,
nRaises=4,
rFactor=.7,
memory=10**5)
p.stopTraining()
players.append(p)
for p in players:
t.addPlayer(p)
#train Player 1 for 1000 hands, training once
players[0].startTraining()
simulate(t, nHands=2000, nTrain=100, nBuyIn=10)
players[0].stopTraining()
#for p in players: p.setBankroll(10**6)
#simulate 20,000 hands and save bankroll history
#bankrolls = simulate(t, nHands=20, nTrain=0, nBuyIn=10)
#plot bankroll history of each player
'''
for i in range(6):
bankroll = bankrolls[i]
plt.plot(range(len(bankroll)), bankroll, label=players[i].getName())
plt.title('Player bankroll vs Hands played')
plt.xlabel('Hands played')
plt.ylabel('Player bankroll/wealth')
from pklearn.templates import simulate, BasicPlayer
from sklearn.ensemble import GradientBoostingRegressor
if __name__ == '__main__':
t = Table(smallBlind=1, bigBlind=2, maxBuyIn=200)
players = []
for i in range(6):
#create BasicPlayer that uses GradientBoostingRegressor as machine learning model
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
r = GradientBoostingRegressor()
name = 'Player ' + str(i+1)
p = BasicPlayer(name=name, reg=r, bankroll=10**6, nRaises=10, rFactor=.7, memory=10**5)
players.append(p)
for p in players: t.addPlayer(p)
#simulate 'nHands' hands
#begin training after 'firstTrain' hands
#before which players take random actions and explore state space
#players train every 'nTrain' hands after 'firstTrain'
#players cash out/ buy in every 'nBuyIn' hands
#table narrates each hands if 'vocal' is True
simulate(t, nHands=10000, firstTrain=2000, nTrain=1000, nBuyIn=10)
simulate(t, nHands=20, nBuyIn=10, vocal=True)
name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name,
reg=r,
bankroll=10**6,
nRaises=10,
rFactor=.7,
memory=10**5)
p.stopTraining()
players.append(p)
for p in players:
t.addPlayer(p)
#train Player 1 for 1000 hands, training once
players[0].startTraining()
simulate(t, nHands=5000, nTrain=1000, nBuyIn=10)
players[0].stopTraining()
#train Player 2 for 10000 hands, training every 1000 hands
players[1].startTraining()
simulate(t, nHands=50000, nTrain=1000, nBuyIn=10)
players[1].stopTraining()
for p in players:
p.setBankroll(10**6)
#simulate 20,000 hands and save bankroll history
bankrolls = simulate(t, nHands=20000, nTrain=0, nBuyIn=10)
#plot bankroll history of each player
for i in range(6):
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name,
bankroll=10**6,
nRaises=10,
rFactor=.7,
memory=10**5)
players.append(p)
for p in players:
t.addPlayer(p)
#simulate 1,000 hands, cashing out/buying in every 10 hands, without training or narrating
simulate(t, nHands=1000, nBuyIn=10, nTrain=0, vocal=False)
features = []
labels = []
for p in players:
features.extend(p.getFeatures())
labels.extend(p.getLabels())
features = np.array(features)
labels = np.array(labels)
#shuffle features/labels
index = np.arange(len(labels))
np.random.shuffle(index)
features = features[index]
t = Table(smallBlind=1, bigBlind=2, maxBuyIn=200)
players = []
for i in range(6):
# create BasicPlayer that uses GradientBoostingRegressor as machine learning model
# with wealth of 1 million and 10 discrete choices for raising,
# with each raise choice .7 times the next largest raise choice
# Player forgets training samples older than 100,000
r = GradientBoostingRegressor()
name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name,
reg=r,
bankroll=10**6,
nRaises=10,
rFactor=.7,
memory=10**5)
players.append(p)
for p in players:
t.addPlayer(p)
# simulate 'nHands' hands
# begin training after 'firstTrain' hands
# before which players take random actions and explore state space
# players train every 'nTrain' hands after 'firstTrain'
# players cash out/ buy in every 'nBuyIn' hands
# table narrates each hands if 'vocal' is True
simulate(t, nHands=10000, firstTrain=2000, nTrain=1000, nBuyIn=10)
simulate(t, nHands=20, nBuyIn=10, vocal=True)
players = []
for i in range(6):
#create BasicPlayer without a machine learning model
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
name = 'Player ' + str(i+1)
p = BasicPlayer(name=name, bankroll=10**6, nRaises=10, rFactor=.7, memory=10**5)
players.append(p)
for p in players: t.addPlayer(p)
#simulate 1,000 hands, cashing out/buying in every 10 hands, without training or narrating
simulate(t, nHands=1000, nBuyIn=10, nTrain=0, vocal=False)
features = []
labels = []
for p in players:
features.extend(p.getFeatures())
labels.extend(p.getLabels())
features = np.array(features)
labels = np.array(labels)
#shuffle features/labels
index = np.arange(len(labels))
np.random.shuffle(index)
features = features[index]
#create BasicPlayer that uses GradientBoostingRegressor as machine learning model
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
r = GradientBoostingRegressor()
name = 'Player ' + str(i+1)
p = BasicPlayer(name=name, reg=r, bankroll=10**6, nRaises=10, rFactor=.7, memory=10**5)
p.stopTraining()
players.append(p)
for p in players: t.addPlayer(p)
#train Player 1 for 1000 hands, training once
players[0].startTraining()
simulate(t, nHands=1000, nTrain=1000, nBuyIn=10)
players[0].stopTraining()
#train Player 2 for 10000 hands, training every 1000 hands
players[1].startTraining()
simulate(t, nHands=10000, nTrain=1000, nBuyIn=10)
players[1].stopTraining()
for p in players: p.setBankroll(10**6)
#simulate 20,000 hands and save bankroll history
bankrolls = simulate(t, nHands=20000, nTrain=0, nBuyIn=10)
#plot bankroll history of each player
for i in range(6):
bankroll = bankrolls[i]
