def update_history(self, ttt: TicTacToe):
reward = 0
if ttt.game_over() and ttt.is_draw():
reward = 0
elif ttt.game_over() and ttt.winner == self.symbol:
reward = 1
else:
reward = -1
self.history.append((ttt.get_state(), reward))
def make_move(self, ttt: TicTacToe):
# explore - make a random move
if np.random.rand() < self.epsilon:
while True:
coord = np.random.randint(3, size=2)
x = coord[0]
y = coord[1]
if ttt.make_move(x, y, self.symbol) or ttt.game_over():
return
# let the network predict the next move
state = ttt.get_state()
one_hot = self.one_hot_encoded(state, ttt)
values = self.model.predict(np.asarray([one_hot]))[0]
high = -1000 # value of field
field = -1 # index of field
for i in range(len(state)): # select best move
if state[i] == 0:
if values[i] > high:
high = values[i].copy()
field = i
x = field % 3
y = field // 3
if ttt.make_move(x, y, self.symbol):
return
else:
raise Exception("dafuq?")
