class QPlayer(Player):
def __init__(self):
super().__init__()
self.exploration_rate = 0.2
self.qnet = QNet()
def take_turn(self, field):
if self.exploration_rate <= np.random.random():
take = self.random_turn(field)
else:
take = self.greedy_turn(field)
return take
def random_turn(self, field):
return np.random.randint(0, 7)
def greedy_turn(self, field):
return np.argmax(self.qnet.predict(field))
class Game:
def __init__(self):
self.discount = 0.8
self.qnet = QNet()
def create_training_pair(self, old_state, action, reward, new_state):
# Ask the model for the Q values of the old state (inference)
states = np.zeros(
(2, old_state.shape[0], old_state.shape[1], old_state.shape[2]))
states[0] = old_state
states[1] = new_state
Q_values = self.qnet.predict(states)
old_state_Q_values = Q_values[0]
# Ask the model for the Q values of the new state (inference)
new_state_Q_values = Q_values[1]
# Real Q value for the action we took. This is what we will train towards.
old_state_Q_values[action] = reward + \
self.discount * np.amax(new_state_Q_values)
return old_state, old_state_Q_values
def minimax(self, field, player, depth=5):
if depth == 0:
return evaluate(position)
bestScore = -100000000
for i in range(0, 7):
if field.isColumnFull(i):
continue
fieldClone = field.clone()
fieldClone.put(i, player)
subScore = -self.minimax(fieldClone, player * -1, depth - 1)
if subScore > bestScore:
bestScore = subScore
return bestScore
from qnet import QNet
from field import Field
import numpy as np
a = Field()
net = QNet()
net.getModel().summary()
for i in range(6 * 7 + 2):
player = 1 if i % 2 == 0 else -1
if player > 0:
x = np.argmax(net.predict(a.getField().reshape((1, 6, 7)))[0])
else:
x = np.random.randint(0, 7)
f1, action, done, reward = a.put(x, player)
print(i)
print(a.getField())
if done:
print("Player " + str(reward) + " won")
break
class Game:
def __init__(self):
self.discount = 0.8
self.qnet = QNet()
def create_training_pair(self, old_state, action, reward, new_state):
# Ask the model for the Q values of the old state (inference)
states = np.zeros(
(2, old_state.shape[0], old_state.shape[1], old_state.shape[2]))