def main():
board_size = 4
max_num_moves = int(board_size ** 2)
state_space_size = int(board_size ** 2 + 1)
conv_layers = []
state_space_size = 128
hidden_layers = [state_space_size, max_num_moves]
la = 0.01
state_manager = StateManager(board_size)
num_simulations = 200
player1 = NeuralActor(conv_layers, hidden_layers, max_num_moves, la, 'sgd')
player2 = NeuralActor(conv_layers, hidden_layers, max_num_moves, la, 'sgd')
mct1 = MCT(player1, num_simulations)
mct2 = MCT(player2, num_simulations)
train = False
if train == True:
start_time = time.time()
for i in range(0, 100):
mct1.play_game(copy.deepcopy(state_manager))
training_data = mct1.get_training_data()
loss = player1.update_Q(training_data)
print(str(i) + " " + str(loss))
player1.store_model('data/16.3')
else:
player1.load_model('data/16.3')
player2.load_model('data/16.3')
win1 = 0
win2 = 0
for i in range(0, 1000):
state_manager = StateManager(board_size)
while not state_manager.player1_won() and not state_manager.player2_won():
if not state_manager.player1_to_move:
move_index = random.randrange(0, board_size ** 2)
while not StateManager.is_legal(move_index, state_manager.string_representation()):
move_index = random.randrange(0, board_size ** 2)
move = state_manager.convert_to_move(move_index)
move = state_manager.convert_to_move(player2.get_action(state_manager.string_representation()))
else:
move_index = random.randrange(0, board_size ** 2)
while not StateManager.is_legal(move_index, state_manager.string_representation()):
move_index = random.randrange(0, board_size ** 2)
move = state_manager.convert_to_move(move_index)
#move = state_manager.convert_to_move(player2.get_action(state_manager.string_representation()))
state_manager.make_move(move)
#state_manager.show()
if state_manager.player1_won():
win1 += 1
elif state_manager.player2_won():
win2 += 1
else:
print("No winner")
print("Times player 1 won: " + str(win1) + ". " + "Times player2 won: " + str(win2))
def get_action(self, state_str):
state = np.fromstring(state_str, np.int8) - 48
state = torch.from_numpy(state).float()
nn_output = self.nn(state) # Forward pass
move_index = torch.argmax(nn_output.data)
while not StateManager.is_legal(move_index, state_str):
nn_output.data[0, move_index] = -1.0
move_index = torch.argmax(nn_output.data)
return move_index.item()
