vacuumDecay/ultimatetictactoe.py

"""
A lot of this code was stolen from Pulkit Maloo (https://github.com/pulkitmaloo/Ultimate-Tic-Tac-Toe)
"""

from vacuumDecay import *
from collections import Counter
import itertools

class TTTState(State):
    def __init__(self, curPlayer=0, generation=0, playersNum=2, board=None, lastMove=-1):
        if type(board) == type(None):
            board = "." * 81
        self.curPlayer = curPlayer
        self.generation = generation
        self.playersNum = playersNum
        self.board = board
        self.last_move = lastMove
        self.possible_goals = [(0, 4, 8), (2, 4, 6)]
        self.possible_goals += [(i, i+3, i+6) for i in range(3)]
        self.possible_goals += [(3*i, 3*i+1, 3*i+2) for i in range(3)]
        self.update_box_won()

    def update_box_won(self):
        state = self.board
        temp_box_win = ["."] * 9
        for b in range(9):
            idxs_box = self.indices_of_box(b)
            box_str = state[idxs_box[0]: idxs_box[-1]+1]
            temp_box_win[b] = self.check_small_box(box_str)
        self.box_won = temp_box_win

    def indices_of_box(self, b):
        return list(range(b*9, b*9 + 9))

    def check_small_box(self, box_str):
        for idxs in self.possible_goals:
            (x, y, z) = idxs
            if (box_str[x] == box_str[y] == box_str[z]) and box_str[x] != ".":
                return box_str[x]
        return "."

    def mutate(self, action):
        newBoard = self.board[:action.data] + ['O','X'][self.curPlayer] + self.board[action.data+1:]
        return TTTState(curPlayer=(self.curPlayer+1)%self.playersNum, playersNum=self.playersNum, board=newBoard, lastMove=action.data)

    def box(self, x, y):
        return index(x, y) // 9


    def next_box(self, i):
        return i % 9

    def indices_of_box(self, b):
        return list(range(b*9, b*9 + 9))

    def index(self, x, y):
        x -= 1
        y -= 1
        return ((x//3)*27) + ((x % 3)*3) + ((y//3)*9) + (y % 3)

    def getAvaibleActions(self):
        if self.last_move == -1:
            for i in range(9*9):
                yield Action(self.curPlayer, i)
            return

        box_to_play = self.next_box(self.last_move)
        idxs = self.indices_of_box(box_to_play)
        if self.box_won[box_to_play] != ".":
            pi_2d = [self.indices_of_box(b) for b in range(9) if self.box_won[b] == "."]
            possible_indices = list(itertools.chain.from_iterable(pi_2d))
        else:
            possible_indices = idxs

        for ind in possible_indices:
            if self.board[ind]=='.':
                yield Action(self.curPlayer, ind)

    #def getScoreFor(self, player):
    #    p = ['O','X'][player]
    #    sco = 5
    #    for w in self.box_won:
    #        if w==p:
    #            sco += 1
    #        elif w!='.':
    #            sco -= 0.5
    #    return 1/sco

    def getPriority(self, score, cascadeMem):
        return self.generation + score*10 - cascadeMem*5 + 100

    def checkWin(self):
        self.update_box_won()
        game_won = self.check_small_box(self.box_won)
        if game_won == '.':
            return None
        return game_won == 'X'

    def __str__(self):
        state = self.board
        acts = list(self.getAvaibleActions())
        if len(acts)<=9:
            for i, act in enumerate(acts):
                state = state[:act.data] + str(i+1) + state[act.data+1:]
        s = []
        for row in range(1, 10):
            row_str = ["|"]
            for col in range(1, 10):
                row_str += [state[self.index(row, col)]]
                if (col) % 3 == 0:
                    row_str += ["|"]
            if (row-1) % 3 == 0:
                s.append("-"*(len(row_str)*2-1))
            s.append(" ".join(row_str))
        s.append("-"*(len(row_str)*2-1))
        return '\n'.join(s)

    def symbToNum(self, b):
        if b == '.':
            return 0.0
        elif b == 'O':
            return -1.0 + 2.0 * self.curPlayer
        else:
            return  1.0 - 2.0 * self.curPlayer


    def getTensor(self, player=None, phase='default'):
        if player==None:
            player = self.curPlayer
        s = ''
        for row in range(1, 10):
            for col in range(1, 10):
                s += self.board[self.index(row, col)]
        return torch.tensor([self.symbToNum(b) for b in s])

    @classmethod
    def getModel(cls, phase='default'):
        return Model()

class Model(nn.Module):
    def __init__(self):
        super().__init__()

        self.smol = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=24,
                kernel_size=(3,3),
                stride=3,
                padding=0,
            ),
            nn.ReLU()
        )
        self.comb = nn.Sequential(
            nn.Conv1d(
                in_channels=24,
                out_channels=8,
                kernel_size=1,
                stride=1,
                padding=0,
            ),
            nn.ReLU()
        )
        self.out = nn.Sequential(
            nn.Linear(9*8, 32),
            nn.ReLU(),
            nn.Linear(32, 8),
            nn.ReLU(),
            nn.Linear(8, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = torch.reshape(x, (1,9,9))
        x = self.smol(x)
        x = torch.reshape(x, (24,9))
        x = self.comb(x)
        x = torch.reshape(x, (-1,))
        y = self.out(x)
        return y

if __name__=="__main__":
    init = TTTState()
    run = NeuralRuntime(init)
    run.game([0,1], 4)


    print("[!] Your knowledge will be assimilated!!!")
    trainer = Trainer(init)
    trainer.train()
    trainer.trainFromTerm(run.head)
    print('[!] I have become smart. Destroyer of human Ultimate-TicTacToe players!')
    trainer.saveToMemoryBank(term)