class Play:
def __init__(self, AIModel, playerRole, inputter=DefaultInputter, displayer=DefaultDisplayer):
self.aiModel = AIModel
self.playerRole = playerRole
self.inputter = inputter
self.displayer = displayer
def Start(self):
self.game = Game()
while not self.game.IsFull():
self.displayer(self.game)
if self.playerRole == self.game.GetCurrentPlayer():
while True:
inputPos = self.inputter()
x, y = self.game.PosToXY(inputPos)
if self.game.IsEmpty(x, y):
break
else:
print("Place already taken up")
continue
else:
x, y = Train.GetNextBestMove(self.aiModel, self.game)
self.game.Play(self.game.GetCurrentPlayer(), x, y)
if self.game.GetWinner() != Game.EMPTY:
self.displayer(self.game)
print("Player %s wins ! " % Game.PLAYER_ATTR[self.game.GetWinner()]['name'])
break
elif self.game.IsFull():
self.displayer(self.game)
print("It's a tie !")
break
def GetNextMove(model : ModelBase, game : Game, selector):
# Inputs:
# Current game status
# Outputs:
# (row, col) predicted by the model
prediction = Train.GetPrediction(model, game)
maxPos = selector(prediction)
return Game.PosToXY(maxPos)
def PlayGames(self, games):
y_true = np.zeros((self.batch, Game.BOARD_SIZE))
for i, game in enumerate(games):
# For each game, calculate y_true
# which means we will try Game.BOARD_SIZE places and find out whether we will win.
for pos in range(Game.BOARD_SIZE):
x, y = Game.PosToXY(pos)
if game.IsEmpty(x, y):
trainedPlayer = game.GetCurrentPlayer()
curGame = game.clone()
curGame.Play(trainedPlayer, x, y)
gameResult, steps = self.PlayGame(curGame)
if gameResult == trainedPlayer:
gameResult = (1 - 0.7) * ((0.5) ** (2 * (steps - 1) / (Game.BOARD_SIZE - game.steps))) + 0.7
else:
gameResult = ((steps - 1) / (Game.BOARD_SIZE - game.steps)) * 0.4
y_true[i, pos] = gameResult
return y_true
