def test():
conf = mconfig.EvaluateConfig()
model = RenjuModel(conf)
model.load('currentModel')
while True:
gameBoard = gameCython.game_state(conf.common.game_board_size)
mctsTree = mcts.Mcts(conf, 1, model)
playerIndex = -1
while True:
print(gameBoard.print_beautiful() + '\n')
if playerIndex < 0:
testerMove = input() # type: str
xstr, ystr = testerMove.split(',')
move = mcts.Hand(x=int(xstr) - 1, y=int(ystr) - 1)
else:
move, policy_ = mctsTree.search_move(autoMoveIntoChild=False)
if not gameBoard.play(move.x, move.y):
break
if gameBoard.finished:
break
playerIndex *= -1
mctsTree.move_to_child(move.x, move.y)
print(gameBoard.print_beautiful() + '\n')
if gameBoard.finished:
print('Game finished! The winner is ' + str(gameBoard.winner))
else:
print('Game didn\' finish normally')
def __init__(self, config, playerIndex, model, parallelSize=16):
self.root_node = MctsNode(config.common.game_board_size)
self.root_node.game_board = game.game_state(
config.common.game_board_size)
self.config = config
self.model = model
self.step = 0
self.loop = asyncio.get_event_loop()
self.playerIndex = playerIndex
self.predictionQueue = Queue(parallelSize)
self.searchingNodes = 0
self.sem = asyncio.Semaphore(parallelSize)
def single_self_train(self):
if self.bestModel == None:
self.bestModel = self.load_model() #Type : RenjuModel
#get a mcts tree model
self.mctsTree = [
mcts.Mcts(self.config, -1, self.bestModel),
mcts.Mcts(self.config, 1, self.bestModel)
]
#start a game
gameBoard = game.game_state(self.config.common.game_board_size)
#store all moves into a list.
moveHistory = list()
policyHistory = list()
playerIndex = -1
#continue the game, until player or board says no more.
while True:
print(gameBoard.print_beautiful() + '\n')
sys.stdout.flush()
mctsIndex = 0
if playerIndex > 0:
mctsIndex = 1
move, policy_ = self.mctsTree[mctsIndex].search_move(
autoMoveIntoChild=False)
if not gameBoard.play(move.x, move.y):
break
moveHistory.append(move)
policyHistory.append(policy_.tolist())
if gameBoard.finished:
break
playerIndex *= -1
for m in self.mctsTree:
m.move_to_child(move.x, move.y)
print(gameBoard.print_beautiful() + '\n')
if gameBoard.finished:
logger.warning('Game finished! The winner is ' +
str(gameBoard.winner))
gameHistory = {
'policy': policyHistory,
'move': moveHistory,
'winner': gameBoard.winner
}
self.save_game_history(gameHistory)
else:
logger.warning('Game didn\' finish normally')
def test():
import configs.normal as mconfig
class mockModel():
def __init__(self):
pass
def predict(self, gamestate):
width = mconfig.CommonConfig().game_board_size
policy = np.random.rand(width, width)
policy /= np.sum(policy)
value = np.resize([0], (gamestate.shape[0]))
# for i in range(gamestate.shape[0]):
# if gamestate[i][1][5][6] > 0.5:
# value[i] = -1
return np.resize(policy, (gamestate.shape[0], width, width)), value
m = mockModel()
# import model
#m = model.RenjuModel(mconfig.EvaluateConfig())
# m.load('currentModel')
#m.load('backupModels/model2018-1-9-8-26-11')
while True:
tm = Mcts(mconfig.EvaluateConfig(), -1, m)
board = game.game_state(
mconfig.EvaluateConfig().common.game_board_size)
#
for i in range(4):
board.play(5, i)
board.play(i * 2, 3)
#board.play(8,8)
print(board.print_beautiful())
tm.set_current_game_state(board)
action, policy = tm.search_move()
print(policy)
print(tm.root_node.childP)
input()
def single_versus(self):
gameBoard = gameCython.game_state(EvaluateConfig().common.game_board_size)
mctsTree = [mcts.Mcts(EvaluateConfig(),-1,self.m1),mcts.Mcts(EvaluateConfig(),1,self.m2)]
playerIndex = -1
while True:
print(gameBoard.print_beautiful() + '\n')
mctsIndex = 0
if playerIndex > 0 :
mctsIndex = 1
move,policy_ = mctsTree[mctsIndex].search_move(autoMoveIntoChild = False)
if not gameBoard.play(move.x ,move.y):
break
if gameBoard.finished:
break
playerIndex*= -1
for m in mctsTree:
m.move_to_child(move.x,move.y)
print(gameBoard.print_beautiful() + '\n')
if gameBoard.finished:
print('Game finished! The winner is ' + str(gameBoard.winner))
return gameBoard.winner
else:
print('Game didn\' finish normally')
def restart_game(self, otherPlayerIndex=-1):
self.game = game_state(self.config.common.game_board_size)
self.mcts = Mcts(modelConfig(), -otherPlayerIndex, self.model)
self.aiplayer = -otherPlayerIndex