def run_episode(self):
examples = []
board = get_random_board()
initial_node = MCTSNode(is_initial=True)
player_blue = NNPlayer(Color.BLUE,
n_simulations=self.n_simulations,
current_node=initial_node,
janggi_net=self.predictor,
temperature_start=1,
temperature_threshold=30,
temperature_end=0.01)
player_red = NNPlayer(Color.RED,
n_simulations=self.n_simulations,
current_node=initial_node,
janggi_net=self.predictor,
temperature_start=1,
temperature_threshold=30,
temperature_end=0.01)
game = Game(player_blue, player_red, board)
while not game.is_finished(self.iter_max):
new_action = game.get_next_action()
game.actions.append(new_action)
if game.current_player == Color.BLUE:
examples.append([
board.get_features(game.current_player, game.round),
player_blue.current_node.get_policy(game.current_player),
Color.BLUE
])
examples.append([
board.get_features(game.current_player,
game.round,
data_augmentation=True),
player_blue.current_node.get_policy(
game.current_player, data_augmentation=True),
Color.BLUE
])
else:
examples.append([
board.get_features(game.current_player,
game.round,
data_augmentation=True),
player_red.current_node.get_policy(game.current_player,
data_augmentation=True),
Color.RED
])
examples.append([
board.get_features(game.current_player, game.round),
player_red.current_node.get_policy(game.current_player),
Color.RED
])
game.board.apply_action(new_action)
game.switch_player()
game.board.invalidate_action_cache(
new_action) # Try to reduce memory usage
game.round += 1
winner = game.get_winner()
set_winner(examples, winner)
return examples
def test_fight2(self):
player_blue = NNPlayer(Color.BLUE,
n_simulations=100,
janggi_net=JanggiNetwork(),
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
player_red = NNPlayer(Color.RED,
n_simulations=100,
janggi_net=JanggiNetwork(),
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
fight(player_blue, player_red, 100)
def test_single_action_nn(self):
n_simulations = 800
player_blue = NNPlayer(Color.BLUE,
n_simulations=n_simulations,
janggi_net=JanggiNetwork(),
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
player_red = NNPlayer(Color.RED,
n_simulations=n_simulations,
janggi_net=JanggiNetwork(),
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
board = get_random_board()
game = Game(player_blue, player_red, board)
game.get_next_action()
def organize_fight(self):
player_red = RandomPlayer(Color.RED)
player_blue = NNPlayer(Color.BLUE,
n_simulations=self.n_simulations,
janggi_net=self.predictor,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
fight(player_blue, player_red, self.iter_max)
player_red = RandomMCTSPlayer(
Color.RED,
n_simulations=self.n_simulations_opponent,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
player_blue = NNPlayer(Color.BLUE,
n_simulations=self.n_simulations,
janggi_net=self.predictor,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
fight(player_blue, player_red, self.iter_max)
def run_episode_raw(args):
print("Starting episode", current_process().name)
begin_time = time.time()
predictor, n_simulations, iter_max = args
board = get_random_board()
initial_node = MCTSNode(is_initial=True)
player_blue = NNPlayer(Color.BLUE,
n_simulations=n_simulations,
current_node=initial_node,
janggi_net=predictor,
temperature_start=1,
temperature_threshold=30,
temperature_end=0.01)
player_red = NNPlayer(Color.RED,
n_simulations=n_simulations,
current_node=initial_node,
janggi_net=predictor,
temperature_start=1,
temperature_threshold=30,
temperature_end=0.01)
game = run_game(board, player_blue, player_red, iter_max)
print("Time Episode: ", time.time() - begin_time)
return game.to_json(initial_node)
def get_player(player_name, color, model_saver):
if player_name == "random_mcts":
return RandomMCTSPlayer(color,
n_simulations=800,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
else:
predictor = JanggiNetwork()
model_saver.load_index_model(predictor, None, player_name)
return NNPlayer(color,
n_simulations=400,
janggi_net=predictor,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
from ia.predictors import FilePredictor
from ia.random_mcts_player import NNPlayer, fight, RandomMCTSPlayer
from janggi.parameters import N_ITERATIONS, DEFAULT_N_SIMULATIONS
from janggi.utils import Color
# Example of command:
# python3 show_match_nn.py --number_simulations 800 --n_iterations 200 --root_file_inference /tmp/showmatch --parallel_mcts True --n_threads_mcts 10
player_blue = NNPlayer(Color.BLUE,
n_simulations=DEFAULT_N_SIMULATIONS,
janggi_net=FilePredictor(),
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01,
print_info=True)
player_red = RandomMCTSPlayer(Color.RED,
n_simulations=16000,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01,
print_info=True)
# player_red = NNPlayer(Color.RED, n_simulations=DEFAULT_N_SIMULATIONS,
# janggi_net=FilePredictor(),
# temperature_start=0.01,
# temperature_threshold=30,
# temperature_end=0.01,
# print_info=True)
fight(player_blue, player_red, N_ITERATIONS, print_board=True)
from ia.predictors import FilePredictor
from ia.random_mcts_player import NNPlayer, fight
from janggi.human_player import HumanPlayer
from janggi.parameters import N_ITERATIONS, DEFAULT_N_SIMULATIONS
from janggi.utils import Color
# Example of command:
# python3 play_against_nn.py --number_simulations 800 --n_iterations 200 --root_file_inference /tmp/showmatch --parallel_mcts True --n_threads_mcts 10
player_blue = HumanPlayer(Color.BLUE)
player_red = NNPlayer(Color.RED, n_simulations=DEFAULT_N_SIMULATIONS,
janggi_net=FilePredictor(),
temperature_start=0.001,
temperature_threshold=30,
temperature_end=0.001,
think_when_other=True, print_info=True)
fight(player_blue, player_red, N_ITERATIONS, print_board=True)
def continuous_learning_once(self):
# First, train
for _ in range(EPOCH_NUMBER_CONTINUOUS):
training_set = []
for example in _raw_to_examples(
self.model_saver.all_episodes_raw_iterators(),
PROP_POPULATION_FOR_LEARNING):
training_set.append(example)
if len(training_set) > N_LAST_GAME_TO_CONSIDER:
if not TRAIN_ON_ALL:
break
self.train(training_set)
training_set = []
self.train(training_set)
# Then, fight!
# old_model = copy.deepcopy(self.predictor)
self.model_saver.load_latest_model(self.old_model, None)
self.old_model.to(DEVICE)
victories = 0
print("Start the fights!")
for i in range(N_FIGHTS):
if i < N_FIGHTS / 2:
print("I am BLUE")
new_player = NNPlayer(Color.BLUE,
n_simulations=self.n_simulations,
janggi_net=self.predictor,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
old_player = NNPlayer(Color.RED,
n_simulations=self.n_simulations,
janggi_net=self.old_model,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
winner = fight(new_player, old_player, self.iter_max)
if winner == Color.BLUE:
victories += 1
else:
print("I am RED")
new_player = NNPlayer(Color.RED,
n_simulations=self.n_simulations,
janggi_net=self.predictor,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
old_player = NNPlayer(Color.BLUE,
n_simulations=self.n_simulations,
janggi_net=self.old_model,
temperature_start=0.01,
temperature_threshold=30,
temperature_end=0.01)
winner = fight(old_player, new_player, self.iter_max)
if winner == Color.RED:
victories += 1
if (victories + N_FIGHTS - i -
1) / N_FIGHTS * 100 < VICTORY_THRESHOLD:
# There is no more hope...
break
victory_percentage = victories / N_FIGHTS * 100
if victory_percentage > VICTORY_THRESHOLD:
# Replace model
print("The model was good enough", victory_percentage)
self.model_saver.save_weights(self.predictor,
optimizer=self.optimizer)
else:
# We do not save the model
print("The model was not good enough", victory_percentage)