def play_series(self, agt1, agt2):
player1_wins = 0
for i in range(self.G):
print("[{}>{}]".format("-" * i, "." * (self.G - i - 1)), end="\r")
game = StateManager(size=self.get_size())
if i == self.G - 1:
print_game = input("Print the last game? (Y/n): ")
else:
print_game = "n"
move_counter = 0
while not game.is_game_over():
game_state = game.get_nn_state()
player_turn = game_state[0]
if player_turn == 1:
predictions = agt1.predict(game_state)
else:
predictions = agt2.predict(game_state)
legal_moves = game.get_legal_moves()
if move_counter < topp_random_moves * 2:
move = random.choice(legal_moves)
else:
move = legal_moves[np.argmax(predictions)]
game.move(move)
move_counter += 1
if print_game.lower() == "y":
game.print_board()
if game.get_winner() == 1:
player1_wins += 1
print("{} won {}/{} against {}.".format(agt1.name, player1_wins,
self.G, agt2.name))
tree = Tree(game)
win_stats = []
RBUF = collections.deque(maxlen=rbuf_max_size)
for i in range(offset, num_of_games + 1):
print("Episode: {}/{}".format(i, num_of_games))
state = tree.root
while (not sm.is_game_over()):
best_child, training_case = agent.uct_search(tree, state,
num_search_games)
RBUF.append(training_case)
sm.move(best_child.move)
state = best_child
if verbose and i == num_of_games:
sm.print_board()
if save_interval > 0 and i == offset:
anet.model.save("./{}/size_{}-ep_{}.h5".format(save_dir, board_size,
i))
print("Saved model to: ./{}/size_{}-ep_{}.h5".format(
save_dir, board_size, i))
train_anet(anet, RBUF)
if save_interval > 0 and (i % save_interval == 0):
anet.model.save("./{}/size_{}-ep_{}.h5".format(save_dir, board_size,
i))
print("Saved model to: ./{}/size_{}-ep_{}.h5".format(
save_dir, board_size, i))
