def ai_vs_mcts(nn_batch, ai_simulation_num, mcts_simulation_num, game_num):
""" Play ai against mcts (with uniform heuristic) only and calculate the ai's winning rate
Args:
nn_batch: the batch number for the version of ResNet used, save in the models folder
ai_simulation_num: simulation number used in AlphaGo
mcts_simulation_num: simluation number used in MCTS
game_num: number of games played
Returns:
percentage of games when AI beats MCTS
"""
uniform_net = UniformPredictionNet(path_to_model='/',
board_dimension=BOARD_DIM)
utils = GoUtils()
count_nn_winning = 0
count_mcts_winning = 0
alphago0 = AlphaGoZero(model_path="../models/batch_" + str(nn_batch),
restored=True)
for i in range(game_num):
print()
print("game number ", i)
game_over = False
board = GoBoard(board_dimension=BOARD_DIM, player=PLAYER_BLACK)
while not game_over:
#AlphaGo with MCTS plays black
if board.player == PLAYER_BLACK:
print("AlphaGo Zero plays")
move = alphago0.play_with_mcts(
board, simulation_number=mcts_simulation_num)
else:
print("MCTS plays")
mcts_play_instance = MCTS(
board,
uniform_net,
utils,
simluation_number=mcts_simulation_num)
move = mcts_play_instance.run_simulations_without_noise()
print("\t move is", move)
_, board = utils.make_move(board=board, move=move)
if utils.is_game_finished(board) or len(
board.game_history) > BOARD_DIM**2 * 2:
game_over = True
winner, winning_by_points = utils.evaluate_winner(
board.board_grid)
if winning_by_points > 0:
if winner == 1:
count_nn_winning += 1
elif winner == -1:
count_mcts_winning += 1
print("winner is ", winner)
print("winning by points", winning_by_points)
print(board)
return count_nn_winning, count_mcts_winning
def ai_vs_random(nn_batch, ai_simulation_num, game_num):
""" Play ai against random play
Args:
nn_batch: the batch number for the version of ResNet used, save in the models folder
ai_simulation_num: simulation number used in AlphaGo
game_num: number of games played
Returns:
percentage of games when AI beats MCTS
"""
uniform_net = UniformPredictionNet(path_to_model='/',
board_dimension=BOARD_DIM)
utils = GoUtils()
count_nn_winning = 0
count_random_winning = 0
alphago0 = AlphaGoZero(model_path="../models/batch_" + str(nn_batch),
restored=True)
for i in range(game_num):
print()
print("game number ", i)
game_over = False
board = GoBoard(board_dimension=BOARD_DIM, player=PLAYER_BLACK)
while not game_over:
#AlphaGo with MCTS plays black
if board.player == PLAYER_BLACK:
print("AlphaGo Zero plays")
move = alphago0.play_with_mcts(
board, simulation_number=mcts_simulation_num)
else:
print("Random plays")
p, _ = uniform_net.predict(board)
move = random.choice(
[move for move in p.keys() if p[move] > 0])
print("\t move is", move)
_, board = utils.make_move(board=board, move=move)
if utils.is_game_finished(board) or len(
board.game_history) > BOARD_DIM**2 * 2:
game_over = True
winner, winning_by_points = utils.evaluate_winner(
board.board_grid)
if winning_by_points > 0:
if winner == 1:
count_nn_winning += 1
elif winner == -1:
count_random_winning += 1
print("winner is ", winner)
print("winning by points", winning_by_points)
print(board)
return count_nn_winning, count_random_winning