def main():
# Use alphazero self-play for data generation
agents_meta = parse_schedule()
# worker variable of main process
board = Board()
sigexit = Event()
sigexit.set() # pre-set signal so main proc generator will iterate only once
# subprocess data generator
helper = DataHelper(data_files=[])
helper.set_agents_meta(agents_meta=agents_meta)
generator = helper.generate_batch(TRAINING_CONFIG["batch_size"])
# start generating
with h5py.File(f"{DATA_CONFIG['data_path']}/latest.train.hdf5", 'a') as hf:
for state_batch, value_batch, probs_batch in generator:
for batch_name in ("state_batch", "value_batch", "probs_batch"):
if batch_name not in hf:
shape = locals()[batch_name].shape
hf.create_dataset(batch_name, (0, *shape), maxshape=(None, *shape))
hf[batch_name].resize(hf[batch_name].shape[0] + 1, axis=0)
hf[batch_name][-1] = locals()[batch_name]
# prevent main proc from generating data too quick
# since sigexit has been set, proc will iterate only once
run_proc(helper.buffer, helper.buffer_size, helper.lock,
sigexit, agents_meta, board)
board.reset()
def angel_move(board: Board, angel: Angel):
"""
(new_board,angel choosen move)
"""
angel_avaliable_move = angel.get_avaliable_move(board)
#Input as 3,4 [which are row index]
angel_choosen_move = None
while angel_choosen_move not in angel_avaliable_move:
print("As an angel, you can move to")
print(angel_avaliable_move)
angel_choosen_move = tuple(int(x.strip()) for x in input().split(','))
#Remove previous move angel have make
board.remove_angel()
board.fill(angel_choosen_move[0], angel_choosen_move[1], ANGLE_SPACE)
angel.move(angel_choosen_move[0], angel_choosen_move[1])
return board, angel_choosen_move
def main():
# Use alphazero self-play for data generation
agents_meta = parse_schedule()
# worker variable of main process
board = Board()
sigexit = Event()
sigexit.set(
) # pre-set signal so main proc generator will iterate only once
# subprocess data generator
helper = DataHelper(data_files=[])
helper.set_agents_meta(agents_meta=agents_meta)
generator = helper.generate_batch(TRAINING_CONFIG["batch_size"])
# start generating
with h5py.File(f"{DATA_CONFIG['data_path']}/latest.train.hdf5", 'a') as hf:
for state_batch, value_batch, probs_batch in generator:
for batch_name in ("state_batch", "value_batch", "probs_batch"):
if batch_name not in hf:
shape = locals()[batch_name].shape
hf.create_dataset(batch_name, (0, *shape),
maxshape=(None, *shape))
hf[batch_name].resize(hf[batch_name].shape[0] + 1, axis=0)
hf[batch_name][-1] = locals()[batch_name]
# prevent main proc from generating data too quick
# since sigexit has been set, proc will iterate only once
run_proc(helper.buffer, helper.buffer_size, helper.lock, sigexit,
agents_meta, board)
board.reset()
def start(width: int, height: int, n: int):
"""
Start the game
width : Width of the board
height : Height of the board
n : How many move the angel wanted to consider from current position
"""
board = Board(width, height)
(angel, devil) = create_character(board, n)
turn = 0
while not is_terminal(board, angel):
turn += 1
angel_position = angel.get_position()
devil_position = devil.get_position()
#Initial random angel move
board.fill(angel_position[0], angel_position[1], ANGLE_SPACE)
#Initial random devil move
board.fill(devil_position[0], devil_position[1], BLOCK_SPACE)
print("Current board")
board.pretty_print()
print("******************")
#Angel turn
print("Angle turn")
board, angel_choosen_move = angel_move(board, angel)
print("Current board")
board.pretty_print()
print("******************")
#Devil turn
print("Devil turn")
board = devil_move(board, angel_choosen_move, devil)
print(f"Game end at turn:{turn}")
def test_reversed(self):
b = Board()
b.grid = self.grid_1
expected = [
[64, 16, 8, 0],
[32, 4, 0, 0],
[4, 2, 0, 0],
[0, 4, 0, 0],
]
self.assertEqual(b.grid, self.grid_1)
b.reverse()
self.assertEqual(b.grid, expected)
def test_transpose(self):
b = Board()
b.grid = self.grid_1
expected = [
[0, 0, 0, 0],
[8, 0, 0, 0],
[16, 4, 2, 4],
[64, 32, 4, 0],
]
self.assertEqual(b.grid, self.grid_1)
b.transpose()
self.assertEqual(b.grid, expected)
def __init__(self, model_file=None):
# Build Network Architecture
input_shape = Board().encoded_states().shape # (6, 15, 15)
inputs = Input(input_shape)
shared_net = Sequential([
*ConvBlock(32, input_shape=input_shape), *ConvBlock(64),
*ConvBlock(128)
], "shared_net")
policy_head = Sequential([
shared_net, *ConvBlock(4, (1, 1), "relu"),
Flatten(),
Dense(Game["board_size"], kernel_regularizer=l2()),
Activation("softmax")
], "policy_head")
value_head = Sequential([
shared_net, *ConvBlock(2, (1, 1), "relu"),
Flatten(),
Dense(64, activation="relu", kernel_regularizer=l2()),
Dense(1, kernel_regularizer=l2()),
Activation("tanh")
], "value_head")
self.model = Model(inputs, [value_head(inputs), policy_head(inputs)])
if model_file is not None:
self.restore_model(model_file)
def _reset(self):
self.board = Board(self.board_shape, max_per_cell=1)
self.phase = self.game_phases[0]
self.state = SeegaState(board=self.board,
next_player=self.first_player,
boring_limit=self.just_stop,
game_phase=self.phase)
self.current_player = self.first_player
def dual_play(agents, board=None, verbose=False, graphic=False):
"""
Play with 2 players.
Params:
agents: { Player.black: agent1, Player.white: agent2 }.
board: initial board state. Start player will be determined here.
verbose: if true, then return value will be in the form of training data.
Returns:
if verbose set to True:
[(state_inputs, final_score, action_probs)]
Each element is a numpy.array.
else:
winner
"""
if board is None:
board = Board()
elif board.status["is_end"]:
board.reset()
if verbose is True:
result = []
else:
result = Player.none
while True:
# set the current agent
cur_agent = agents[board.status["cur_player"]]
# evaluate board state and get action
if verbose is True:
_, action_probs, next_move = cur_agent.eval_state(board)
result.append([
board.encoded_states(),
board.status["cur_player"],
action_probs
])
else:
next_move = cur_agent.get_action(board)
# update board
board.apply_move(next_move)
if graphic:
print(board)
# end judge
if board.status["is_end"]:
winner = board.status["winner"]
if graphic:
print("Game ends. winner is {}.".format(winner))
# format output result
if verbose is True:
result = [(
state[0],
np.array(Player.calc_score(state[1], winner)),
state[2]
) for state in result]
else:
result = winner
return result
def _reset(self):
self.done = False
self.board = Board(self.board_shape)
self.state = SeegaState(board=self.board,
next_player=self.player_color,
boring_limit=self.just_stop)
# self.trace = Trace(
# self.state, players={-1: self.players[-1].name, 1: self.players[1].name}
# )
self.current_player = self.player_color
self._fill_board()
def devil_move(board: Board, angel_choosen_move: tuple, devil: Devil):
"""
Return new board
"""
devil.set_angel_position(angel_choosen_move)
devil_avaliable_move = devil.get_avaliable_move(board)
devil_choosen_move = None
while devil_choosen_move not in devil_avaliable_move:
print("As an devil, you can move to")
print(devil_avaliable_move)
devil_choosen_move = tuple(int(x.strip()) for x in input().split(','))
board.fill(devil_choosen_move[0], devil_choosen_move[1], BLOCK_SPACE)
return board
def __init__(self, model_name=None):
with tf.variable_scope("Dataset"):
input_shape = Board().encoded_states().shape # (6, 15, 15)
self.iter = tf.data.Iterator.from_structure(
(tf.float32, tf.float32, tf.float32), (tf.TensorShape(
(None, *input_shape)), tf.TensorShape(
(None, )), tf.TensorShape((None, Game["board_size"]))))
self.mini_batch = self.iter.get_next()
self.inputs, self.state_value, self.action_probs = self.mini_batch
inputs_t = tf.transpose(self.inputs, [0, 2, 3, 1]) # channel_last
self.inputs_t = inputs_t
with tf.variable_scope("SharedNet"):
conv_blocks = []
for i in range(3):
conv_blocks.append(
tf.layers.conv2d(
inputs=inputs_t if i == 0 else conv_blocks[i - 1],
filters=2**(i + 5), # 32, 64, 128
kernel_size=(3, 3),
padding="same",
activation=tf.nn.relu,
name="conv_{}".format(i)))
shared_output = conv_blocks[-1]
with tf.variable_scope("PolicyHead"):
policy_conv = tf.layers.conv2d(inputs=shared_output,
filters=4,
kernel_size=(1, 1),
padding="same",
activation=tf.nn.relu)
policy_flatten = tf.layers.flatten(policy_conv)
self.policy_logits = tf.layers.dense(policy_flatten,
Game["board_size"])
self.policy_output = tf.nn.softmax(self.policy_logits,
name="policy_output")
with tf.variable_scope("ValueHead"):
value_conv = tf.layers.conv2d(inputs=shared_output,
filters=2,
kernel_size=(1, 1),
padding="same",
activation=tf.nn.relu)
value_flatten = tf.layers.flatten(value_conv)
value_hidden = tf.layers.dense(value_flatten, 64, tf.nn.relu)
value_logits = tf.layers.dense(value_hidden, 1)
self.value_output = tf.reshape(tf.nn.tanh(value_logits), [-1],
name="value_output")
self.session = tf.Session()
self.saver = tf.train.Saver()
self.model_file = self._parse_path(model_name)
self.initialized = False
def eval_agents(agents, num_games=9):
"""
Eval the performance of two agents by multiple game simulation.
Params:
agents: [agent1, agent2]
num_games: number of games simulated, default to BO9.
board: a pre-init board can be passed to avoid re-construct.
Returns:
[win_rate(a) for a in agents]
"""
print("---------Evaluating agents-------------")
board = Board()
players = [Player.black, Player.white]
win_cnts = np.zeros(2)
for i in range(num_games):
winner = dual_play(dict(zip(players, agents)), board)
try:
win_idx = players.index(winner)
win_cnts[win_idx] += 1
print("Round {} ends, winner is <{}: {}>;".format(i + 1, winner, agents[win_idx]))
except ValueError: # tie
win_cnts += 0.5
print("Round {} ends, tie game;".format(i + 1))
players.reverse() # exchange the start player
board.reset()
[agent.reset() for agent in agents]
win_rates = win_cnts / num_games
print("Win rate:")
print("{}: {}".format(agents[0], win_rates[0]))
print("{}: {}".format(agents[1], win_rates[1]))
print("---------------------------------------")
return tuple(win_rates)
def run_proc(buffer, maxlen, lock, sigexit, agents_meta, board=None):
"""
Multiprocessing target funcion
"""
if board is None:
board = Board()
agents = parse_agents_meta(agents_meta)
while True:
data = simulate_game_data(board, agents)
print(f"Finished one episode with {len(data)} samples.")
with lock:
if len(buffer) >= maxlen:
buffer = buffer[len(buffer) - maxlen:]
buffer.extend(data)
if sigexit.is_set():
return
def test_spawn_minimum_0(self):
b = Board(self.seed)
b.spawn_minimum()
expected = self.initial_grid[:]
expected[1][0] = 2
self.assertEqual(b.grid, expected)
b.spawn_minimum()
expected[3][0] = 2
self.assertEqual(b.grid, expected)
def test_left(self):
b = Board(self.seed)
b.left()
expected = [
[0, 0, 0, 0],
[2, 0, 0, 0], # spawn
[2, 0, 0, 0],
[0, 0, 0, 0],
]
self.assertEqual(b.grid, expected)
b = Board(self.seed)
b.grid = self.grid_2
b.left()
expected = [
[8, 0, 0, 0],
[4, 2, 0, 0],
[16, 4, 0, 2], # spawn
[8, 0, 0, 0],
]
self.assertEqual(b.grid, expected)
def test_squash_0(self):
b = Board()
arr = [0, 2, 0, 4]
self.assertEqual(b.squash(arr), [2, 4, 0, 0])
arr = [8, 8, 8, 8]
self.assertEqual(b.squash(arr), [16, 16, 0, 0])
arr = [2, 2, 4, 4]
self.assertEqual(b.squash(arr), [4, 8, 0, 0])
arr = [2, 4, 8, 4]
self.assertEqual(b.squash(arr), [2, 4, 8, 4])
def __init__(self, shape, current_player=-1, parent=None):
super(BoardGUI, self).__init__(parent)
self.current_player = current_player
self.color = ["black", "green"]
self.score = {-1: 0, 1: 0}
self.shape = shape
self.setFixedSize(100 * shape[1], 100 * shape[0])
grid_layout = QGridLayout()
grid_layout.setSpacing(0)
self.squares = list()
self._board = Board(shape)
for i in range(shape[0]):
temp = list()
for j in range(shape[1]):
square = Square(i, j)
grid_layout.addWidget(square, shape[0] - i, j)
temp.append(square)
self.squares.append(temp)
self.set_default_colors()
self.setLayout(grid_layout)
def __init__(self, filename=request("dialog.new_map_dialog.filename")):
self.filename = filename
title = gui.Label("New map")
self.table = table = gui.Table()
table.tr()
table.td(gui.Label("Width"), style=td_style)
table.td(gui.Label("Height"), style=td_style)
table.tr()
self.width_input = gui.Input(str(
request("dialog.new_map_dialog.board_width")),
size=5)
table.td(self.width_input, style=td_style)
self.height_input = gui.Input(
request("dialog.new_map_dialog.board_height"), size=5)
table.td(self.height_input, style=td_style)
add_event_handler(self.width_input, enter_pressed,
lambda e: self.new_size())
add_event_handler(self.height_input, enter_pressed,
lambda e: self.new_size())
table.tr()
show_button = gui.Button('Show')
show_button.connect(gui.CLICK, self.new_size)
table.td(show_button, style=td_style)
self.board = Board.new_rectangle(
request("dialog.new_map_dialog.board_width"),
request("dialog.new_map_dialog.board_height"))
self.preview = Preview()
table.tr()
table.td(self.preview, style=td_style)
self.preview.set_map(self.board)
table.tr()
self.filename_input = gui.Input(self.filename)
table.td(self.filename_input)
table.tr()
button = gui.Button(' Ok ')
table.td(button, style=td_style)
button.connect(gui.CLICK, self.ok_clicked)
gui.Dialog.__init__(self, title, table)
def test_empty_cells_0(self):
b = Board(self.seed)
empty_cells = b.empty_cells()
expected = {(i, j) for i in range(4) for j in range(4)}
expected.remove((2, 2))
self.assertEqual(empty_cells, expected)
def get_action(self, state: Board) -> Position:
""" Simply get the next position according to current state. """
# if not overrided, simply return random move
return state.random_move()
def test_empty_cells_1(self):
b = Board()
b.grid = self.grid_1
empty_cells = b.empty_cells()
expected = {(0, 0), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1), (3, 3)}
self.assertEqual(empty_cells, expected)
from core import Board, Player, Evaluator
def show_board(board):
black_stonenum = len([1 for i in board.stones if i == -1])
white_stonenum = len([1 for i in board.stones if i == 1])
print("black: {0} vs {1} :white".format(black_stonenum, white_stonenum))
dispboard = ["2" if stone == -1 else "1" if stone == 1 else " " for stone in board.stones]
for i in xrange(0, 8):
offset = i * 8
print(dispboard[offset:offset+8])
if __name__ == '__main__':
evaluator = Evaluator()
player = Player(evaluator)
board = Board()
while not board.check_gameover():
mpos = player.move(board, -1)
show_board(board)
epos = player.move(board, 1)
show_board(board)
def resource_path(relative_path):
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
except Exception:
base_path = os.path.abspath(".")
return os.path.join(base_path, relative_path)
if __name__ == "__main__":
setting = Setting(resource_path("setting.config"))
screen_resolution = setting.get_screen_resolution()
grid = setting.get_grid()
pygame.init()
game_logic = GameLogic(Board(grid[0], grid[1]), setting.get_n())
game_logic.init()
screen = pygame.display.set_mode(screen_resolution)
cell_size = get_cell_size(screen_resolution[0], screen_resolution[1],
game_logic.get_board().get_size())
game_loop(screen, cell_size, game_logic)
def ok_clicked(self):
self.value = Board.new_rectangle(int(self.width_input.value),
int(self.height_input.value))
self.filename = preference.map_filename(self.filename_input.value)
self.send(gui.CHANGE)
def test_calc_score(self):
b = Board()
b.grid = self.grid_1
self.assertEqual(b.calc_score(), 134)
def new_size(self):
self.board = Board.new_rectangle(int(self.width_input.value),
int(self.height_input.value))
self.preview.set_map(self.board)
def test_init_board(self):
b = Board(self.seed)
grid = b.grid
self.assertEqual(grid, self.initial_grid)
import readline import code from core import Board b = Board(seed=0) variables = globals().copy() variables.update(locals()) shell = code.InteractiveConsole(variables) shell.interact()
def test_check_stalemate(self):
b = Board()
self.assertFalse(b.check_stalemate())
b.grid = [
[2, 2, 2, 2],
[2, 2, 2, 0],
[2, 2, 2, 2],
[2, 2, 2, 2],
]
self.assertFalse(b.check_stalemate())
b.grid[1][3] = 2
self.assertFalse(b.check_stalemate())
b.grid = [
[2, 4, 2, 4],
[2, 4, 2, 4],
[2, 4, 2, 4],
[2, 4, 2, 4],
]
self.assertFalse(b.check_stalemate())
b.grid = [
[2, 4, 2, 4],
[4, 2, 4, 2],
[2, 4, 2, 4],
[4, 2, 4, 2],
]
self.assertTrue(b.check_stalemate())
def test_spawn_minimum_1(self):
b = Board(self.seed)
b.grid = self.grid_stalemate
b.spawn_minimum()
self.assertTrue(b.gameover)
