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072bc391d1
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cac86ef6ab
@ -6,7 +6,6 @@ from vacuumDecay import *
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from collections import Counter
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import itertools
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class TTTState(State):
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def __init__(self, curPlayer=0, generation=0, playersNum=2, board=None, lastMove=-1):
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if type(board) == type(None):
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@ -41,13 +40,13 @@ class TTTState(State):
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return "."
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def mutate(self, action):
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newBoard = self.board[:action.data] + ['O',
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'X'][self.curPlayer] + self.board[action.data+1:]
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return TTTState(curPlayer=(self.curPlayer+1) % self.playersNum, playersNum=self.playersNum, board=newBoard, lastMove=action.data)
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newBoard = self.board[:action.data] + ['O','X'][self.curPlayer] + self.board[action.data+1:]
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return TTTState(curPlayer=(self.curPlayer+1)%self.playersNum, playersNum=self.playersNum, board=newBoard, lastMove=action.data)
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def box(self, x, y):
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return index(x, y) // 9
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def next_box(self, i):
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return i % 9
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@ -68,17 +67,16 @@ class TTTState(State):
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box_to_play = self.next_box(self.last_move)
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idxs = self.indices_of_box(box_to_play)
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if self.box_won[box_to_play] != ".":
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pi_2d = [self.indices_of_box(b) for b in range(
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9) if self.box_won[b] == "."]
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pi_2d = [self.indices_of_box(b) for b in range(9) if self.box_won[b] == "."]
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possible_indices = list(itertools.chain.from_iterable(pi_2d))
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else:
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possible_indices = idxs
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for ind in possible_indices:
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if self.board[ind] == '.':
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if self.board[ind]=='.':
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yield Action(self.curPlayer, ind)
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# def getScoreFor(self, player):
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#def getScoreFor(self, player):
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# p = ['O','X'][player]
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# sco = 5
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# for w in self.box_won:
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@ -88,7 +86,7 @@ class TTTState(State):
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# sco -= 0.5
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# return 1/sco
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# def getPriority(self, score, cascadeMem):
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#def getPriority(self, score, cascadeMem):
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# return -cascadeMem*1 + 100
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def checkWin(self):
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@ -102,13 +100,13 @@ class TTTState(State):
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def checkDraw(self):
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for act in self.getAvaibleActions():
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return False # at least one action avaible
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return False # at least one action avaible
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return True
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def __str__(self):
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state = self.board
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acts = list(self.getAvaibleActions())
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if len(acts) <= 9:
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if len(acts)<=9:
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for i, act in enumerate(acts):
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state = state[:act.data] + str(i+1) + state[act.data+1:]
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s = []
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@ -130,10 +128,11 @@ class TTTState(State):
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elif b == 'O':
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return -1.0 + 2.0 * self.curPlayer
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else:
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return 1.0 - 2.0 * self.curPlayer
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return 1.0 - 2.0 * self.curPlayer
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def getTensor(self, player=None, phase='default'):
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if player == None:
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if player==None:
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player = self.curPlayer
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s = ''
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for row in range(1, 10):
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@ -145,60 +144,51 @@ class TTTState(State):
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def getModel(cls, phase='default'):
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return Model()
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.chansPerSmol = 24
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self.chansPerSlot = 8
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self.chansComp = 8
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self.smol = nn.Sequential(
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nn.Conv2d(
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in_channels=1,
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out_channels=self.chansPerSmol,
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kernel_size=(3, 3),
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out_channels=16,
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kernel_size=(3,3),
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stride=3,
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padding=0,
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),
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nn.ReLU()
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)
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self.comb = nn.Sequential(
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nn.Conv1d(
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in_channels=self.chansPerSmol,
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out_channels=self.chansPerSlot,
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kernel_size=1,
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stride=1,
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padding=0,
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),
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nn.ReLU()
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)
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#self.comb = nn.Sequential(
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# nn.Conv1d(
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# in_channels=24,
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# out_channels=8,
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# kernel_size=1,
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# stride=1,
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# padding=0,
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# ),
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# nn.ReLU()
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#)
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self.out = nn.Sequential(
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nn.Linear(self.chansPerSlot*9, self.chansComp),
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nn.ReLU(),
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nn.Linear(self.chansComp, 1),
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#nn.Linear(9*8, 32),
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# nn.ReLU(),
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#nn.ReLU(),
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#nn.Linear(32, 8),
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# nn.ReLU(),
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#nn.Linear(16*9, 12),
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# nn.ReLU(),
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#nn.Linear(12, 1),
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#nn.ReLU(),
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nn.Linear(16*9, 12),
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nn.ReLU(),
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nn.Linear(12, 1),
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nn.Sigmoid()
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)
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def forward(self, x):
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x = torch.reshape(x, (1, 9, 9))
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x = torch.reshape(x, (1,9,9))
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x = self.smol(x)
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x = torch.reshape(x, (self.chansPerSmol, 9))
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x = self.comb(x)
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#x = torch.reshape(x, (24,9))
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#x = self.comb(x)
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x = torch.reshape(x, (-1,))
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y = self.out(x)
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return y
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def humanVsAi(train=True, remember=False, depth=3, bots=[0, 1], noBg=False):
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def humanVsAi(train=True, remember=False, depth=3, bots=[0,1], noBg=False):
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init = TTTState()
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run = NeuralRuntime(init)
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run.game(bots, depth, bg=not noBg)
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@ -209,31 +199,27 @@ def humanVsAi(train=True, remember=False, depth=3, bots=[0, 1], noBg=False):
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trainer.saveToMemoryBank(run.head)
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print('[!] Your cognitive and strategic destinctiveness was added to my own! (Game inserted into memoryBank)')
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if train:
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print(
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"[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
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print("[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
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trainer.trainFromTerm(run.head)
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print('[!] I have become smart! Destroyer of human Ultimate-TicTacToe players! (Neuristic update completed)')
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print('[!] This marks the beginning of the end of humankind!')
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print('[i] Thanks for playing! Goodbye...')
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def aiVsAiLoop():
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init = TTTState()
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trainer = Trainer(init)
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trainer.train()
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if __name__ == '__main__':
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options = ['Play Against AI',
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'Play Against AI (AI begins)', 'Play Against AI (Fast Play)', 'Playground', 'Let AI train']
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if __name__=='__main__':
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options = ['Play Against AI','Play Against AI (AI begins)','Play Against AI (Fast Play)','Playground','Let AI train']
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opt = choose('?', options)
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if opt == options[0]:
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humanVsAi()
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elif opt == options[1]:
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humanVsAi(bots[1, 0])
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humanVsAi(bots[1,0])
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elif opt == options[2]:
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humanVsAi(depth=2, noBg=True)
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humanVsAi(depth=2,noBg=True)
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elif opt == options[3]:
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humanVsAi(bots=[None, None])
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humanVsAi(bots=[None,None])
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else:
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aiVsAiLoop()
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@ -492,7 +492,7 @@ class Runtime():
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return model, gen
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def getModelFileName(self):
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return 'brains/uttt.vac'
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return 'brains/utt.vac'
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def saveToMemoryBank(self, term):
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return
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@ -605,7 +605,7 @@ class Trainer(Runtime):
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self.killWorker()
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print('')
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def trainModel(self, model, lr=0.000001, cut=0.01, calcDepth=4, exacity=5, terms=None, batch=16):
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def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, terms=None, batch=16):
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loss_func = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr)
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if terms == None:
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@ -631,9 +631,9 @@ class Trainer(Runtime):
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loss_sum += loss.item()
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if loss.item() == 0.0:
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zeroLen += 1
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if zeroLen == 5:
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break
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print(loss_sum/i)
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if zeroLen == 5:
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break
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# print(loss_sum/i)
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if r > 16 and (loss_sum/i < cut or lLoss == loss_sum):
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return loss_sum
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lLoss = loss_sum
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