def worker_routine(game, w2s_conn, public_q):
commands = np.argwhere(np.ones((6, 5, 6)) == 1)
inf_helper = InfHelper(w2s_conn)
search = mcts.MCTSearch(game, inf_helper, commands)
accumulated_data = []
winner = None
while True:
action_command, training_data = search.start_search(300)
accumulated_data.append(training_data)
is_turn_end = game.take_command(action_command)
if is_turn_end:
game.turn_end(verbose=False)
if game.is_terminal:
game.final_score(verbose=True)
w2s_conn.send([True] * 3)
winner = game.leading_player_num
print('end in', game.turn)
break
else:
game.start_turn()
if game.turn >= 11:
w2s_conn.send([True] * 3)
game.final_score()
winner = game.leading_player_num
print('exceeding turn 10')
break
search = mcts.MCTSearch(game, inf_helper, commands)
else:
##########################
search.change_root()
# search = mcts.MCTSearch(game, inf_helper, commands)
#########################
state_data, action_data, value_data, mask_data = [], [], [], []
for state, action_index, player, mask in accumulated_data:
state_data.append(state)
action_data.append(action_index)
mask_data.append(mask)
if player == winner:
value_data.append(1.)
else:
value_data.append(-1.)
public_q.put((state_data, action_data, value_data, mask_data))
def debug():
game = azul.Azul(2)
game.start()
commands = np.argwhere(np.ones((6, 5, 6)) == 1)
inf_helper = InfHelperS()
search = mcts.MCTSearch(game, inf_helper, commands)
searches = [search]
accumulated_data = []
winner = None
while True:
action_command, training_data = search.start_search(300)
accumulated_data.append(training_data)
is_turn_end = game.take_command(action_command)
if is_turn_end:
game.turn_end(verbose=False)
if game.is_terminal:
game.final_score()
winner = game.leading_player_num
break
else:
game.start_turn()
search = mcts.MCTSearch(game, inf_helper, commands)
else:
# search.change_root()
search = mcts.MCTSearch(game, inf_helper, commands)
searches.append(search)
state_data, action_data, value_data = [], [], []
for state, action_index, player in accumulated_data:
state_data.append(state)
action_index = str(action_index // 30) + str(
(action_index % 30) // 6) + str(action_index % 6)
action_data.append(action_index)
if player == winner:
value_data.append(1.)
else:
value_data.append(-1.)
return state_data, action_data, value_data, searches
import azul
import policy
import mcts
if __name__ == '__main__':
game = azul.Azul(2)
game.start()
# states = game.states()
# mask = game.flat_mask()
# inf = alphazul.InferenceNetwork(states.shape[0],mask.shape[0])
# value, prior = inf.predict([states],[mask])
# print(prior,np.sum(prior))
# print(np.sum(prior>0),np.sum(mask))
search = mcts.MCTSearch(game,
policy.rollout,
commands=np.argwhere(np.ones((6, 5, 6)) == 1))
while True:
# game.display()
# print('------------------------------------')
# search._root.game.display()
# print('\n\n')
action, _ = search.start_search(100)
is_turn_end = game.take_command(action)
if is_turn_end:
game.turn_end(verbose=False)
break
else:
search.change_root()
def mcts_roolout(game):
commands = np.argwhere(np.ones((6, 5, 6)) == 1)
search = mcts.MCTSearch(game, rollout, commands)
action = search.start_search_deterministic(300)
return action
def __call__(self,game):
commands = np.argwhere(np.ones((6,5,6))==1)
search = mcts.MCTSearch(game,self._infhelper,commands)
action = search.start_search_deterministic(300)
return action