def _contest(self, player1, player2, first_player):
# old model play with new model
players = [player2, None, player1]
player_index = first_player
gomoku = Gomoku(self.n, self.n_in_row, first_player)
self.gomoku_gui.reset_status()
while True:
player = players[player_index + 1]
# select best move
prob = player.get_action_probs(gomoku)
best_move = int(np.argmax(np.array(list(prob))))
# execute move
gomoku.execute_move(best_move)
self.gomoku_gui.execute_move(player_index, best_move)
# check game status
ended, winner = gomoku.get_game_status()
if ended == 1:
return winner
# update search tree
player1.update_with_move(best_move)
player2.update_with_move(best_move)
# next player
player_index = -player_index
def self_play(self, first_color):
"""
This function executes one episode of self-play, starting with player 1.
As the game is played, each turn is added as a training example to
train_examples. The game is played till the game ends. After the game
ends, the outcome of the game is used to assign values to each example
in train_examples.
"""
train_examples = []
gomoku = Gomoku(self.n, self.n_in_row, first_color)
mcts = MCTS("./models/checkpoint.pt", self.thread_pool_size,
self.c_puct, self.num_mcts_sims, self.c_virtual_loss,
self.action_size, self.mcts_use_gpu)
episode_step = 0
while True:
episode_step += 1
# prob
temp = self.temp if episode_step <= self.explore_num else 0
prob = np.array(list(mcts.get_action_probs(gomoku, temp)))
# generate sample
board = tuple_2d_to_numpy_2d(gomoku.get_board())
last_action = gomoku.get_last_move()
cur_player = gomoku.get_current_color()
sym = self.get_symmetries(board, prob)
for b, p in sym:
train_examples.append([b, last_action, cur_player, p])
# dirichlet noise
legal_moves = list(gomoku.get_legal_moves())
noise = 0.25 * np.random.dirichlet(
self.dirichlet_alpha * np.ones(np.count_nonzero(legal_moves)))
prob_noise = 0.75 * prob
j = 0
for i in range(len(prob_noise)):
if legal_moves[i] == 1:
prob_noise[i] += noise[j]
j += 1
prob_noise /= np.sum(prob_noise)
action = np.random.choice(len(prob_noise), p=prob_noise)
# execute move
gomoku.execute_move(action)
mcts.update_with_move(action)
# is ended
ended, winner = gomoku.get_game_status()
if ended == 1:
# b, last_action, cur_player, p, v
return [(x[0], x[1], x[2], x[3], x[2] * winner)
for x in train_examples]
def play_with_human(self,
human_first=True,
checkpoint_name="best_checkpoint"):
t = threading.Thread(target=self.gomoku_gui.loop)
t.start()
# load best model
libtorch_best = NeuralNetwork('./models/best_checkpoint.pt',
self.libtorch_use_gpu,
self.num_mcts_threads * 2)
mcts_best = MCTS(libtorch_best, self.num_mcts_threads * 2, self.c_puct,
self.num_mcts_sims * 4, self.c_virtual_loss,
self.action_size)
# create gomoku game
human_color = self.gomoku_gui.get_human_color()
gomoku = Gomoku(self.n, self.n_in_row,
human_color if human_first else -human_color)
players = ["alpha", None, "human"
] if human_color == 1 else ["human", None, "alpha"]
player_index = human_color if human_first else -human_color
while True:
player = players[player_index + 1]
# select move
if player == "alpha":
prob = mcts_best.get_action_probs(gomoku)
best_move = int(np.argmax(np.array(list(prob))))
self.gomoku_gui.execute_move(player_index, best_move)
else:
self.gomoku_gui.set_is_human(True)
# wait human action
while self.gomoku_gui.get_is_human():
time.sleep(0.1)
best_move = self.gomoku_gui.get_human_move()
# execute move
gomoku.execute_move(best_move)
# check game status
ended, winner = gomoku.get_game_status()
if ended == 1:
break
# update tree search
mcts_best.update_with_move(best_move)
# next player
player_index = -player_index
print("HUMAN WIN" if winner == human_color else "ALPHA ZERO WIN")
t.join()
def _contest(self, network1, network2, first_player, show):
# create MCTS
player1 = MCTS(network1, self.num_mcts_threads, self.c_puct,
self.num_mcts_sims, self.c_virtual_loss,
self.action_size)
player2 = MCTS(network2, self.num_mcts_threads, self.c_puct,
self.num_mcts_sims, self.c_virtual_loss,
self.action_size)
# prepare
players = [player2, None, player1]
player_index = first_player
gomoku = Gomoku(self.n, self.n_in_row, first_player)
if show:
self.gomoku_gui.reset_status()
# play
while True:
player = players[player_index + 1]
# select best move
prob = player.get_action_probs(gomoku)
best_move = int(np.argmax(np.array(list(prob))))
# execute move
gomoku.execute_move(best_move)
if show:
self.gomoku_gui.execute_move(player_index, best_move)
# check game status
ended, winner = gomoku.get_game_status()
if ended == 1:
return winner
# update search tree
player1.update_with_move(best_move)
player2.update_with_move(best_move)
# next player
player_index = -player_index
def self_play(self, first_color, libtorch, show):
"""
This function executes one episode of self-play, starting with player 1.
As the game is played, each turn is added as a training example to
train_examples. The game is played till the game ends. After the game
ends, the outcome of the game is used to assign values to each example
in train_examples.
"""
train_examples = []
player1 = MCTS(libtorch, self.num_mcts_threads, self.c_puct,
self.num_mcts_sims, self.c_virtual_loss,
self.action_size)
player2 = MCTS(libtorch, self.num_mcts_threads, self.c_puct,
self.num_mcts_sims, self.c_virtual_loss,
self.action_size)
players = [player2, None, player1]
player_index = 1
gomoku = Gomoku(self.n, self.n_in_row, first_color)
if show:
self.gomoku_gui.reset_status()
episode_step = 0
while True:
episode_step += 1
player = players[player_index + 1]
# get action prob
if episode_step <= self.num_explore:
prob = np.array(
list(player.get_action_probs(gomoku, self.temp)))
else:
prob = np.array(list(player.get_action_probs(gomoku, 0)))
# generate sample
board = tuple_2d_to_numpy_2d(gomoku.get_board())
last_action = gomoku.get_last_move()
cur_player = gomoku.get_current_color()
sym = self.get_symmetries(board, prob, last_action)
for b, p, a in sym:
train_examples.append([b, a, cur_player, p])
# dirichlet noise
legal_moves = list(gomoku.get_legal_moves())
noise = 0.1 * np.random.dirichlet(
self.dirichlet_alpha * np.ones(np.count_nonzero(legal_moves)))
prob = 0.9 * prob
j = 0
for i in range(len(prob)):
if legal_moves[i] == 1:
prob[i] += noise[j]
j += 1
prob /= np.sum(prob)
# execute move
action = np.random.choice(len(prob), p=prob)
if show:
self.gomoku_gui.execute_move(cur_player, action)
gomoku.execute_move(action)
player1.update_with_move(action)
player2.update_with_move(action)
# next player
player_index = -player_index
# is ended
ended, winner = gomoku.get_game_status()
if ended == 1:
# b, last_action, cur_player, p, v
return [(x[0], x[1], x[2], x[3], x[2] * winner)
for x in train_examples]