def backup(self, value):
if self.previous_state is not None and self.learning:
self.net.fit(c4.getNeuralInput(self.previous_state).reshape(
1, 126),
value,
batch_size=1,
nb_epoch=1)
def greedy(self, state, player=1):
max_value = float("-inf")
next_move = None
# TODO: implemen get_possible_moves in c4
for move in range(7):
if c4.isValidMove(state, move):
new_state = c4.makeMove(state, player, move)
val = self.net.predict(c4.getNeuralInput(new_state).reshape(1, 126), batch_size=1)
if val > max_value:
max_value = val
next_move = move
self.backup(max_value)
return next_move
def greedy(self, state, player=1):
max_value = float("-inf")
next_move = None
# TODO: implemen get_possible_moves in c4
for move in range(7):
if c4.isValidMove(state, move):
new_state = c4.makeMove(state, player, move)
val = self.net.predict(c4.getNeuralInput(new_state).reshape(
1, 126),
batch_size=1)
if val > max_value:
max_value = val
next_move = move
self.backup(max_value)
return next_move
def backup(self, value):
if self.previous_state is not None and self.learning:
self.net.fit(c4.getNeuralInput(self.previous_state).reshape(1, 126), value, batch_size=1, nb_epoch=1)
