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Different net arch (back to fancy conv)

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Dominik Moritz Roth 2022-05-18 19:44:56 +02:00
parent 9c6077e213
commit 072bc391d1
1 changed files with 54 additions and 40 deletions
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94
ultimatetictactoe.py
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@ -6,6 +6,7 @@ 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):
@ -40,13 +41,13 @@ class TTTState(State):
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)
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
@ -67,16 +68,17 @@ class TTTState(State):
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] == "."]
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]=='.':
if self.board[ind] == '.':
yield Action(self.curPlayer, ind)
#def getScoreFor(self, player):
# def getScoreFor(self, player):
# p = ['O','X'][player]
# sco = 5
# for w in self.box_won:
@ -86,7 +88,7 @@ class TTTState(State):
# sco -= 0.5
# return 1/sco
#def getPriority(self, score, cascadeMem):
# def getPriority(self, score, cascadeMem):
# return -cascadeMem*1 + 100
def checkWin(self):
@ -100,13 +102,13 @@ class TTTState(State):
def checkDraw(self):
for act in self.getAvaibleActions():
return False # at least one action avaible
return False # at least one action avaible
return True
def __str__(self):
state = self.board
acts = list(self.getAvaibleActions())
if len(acts)<=9:
if len(acts) <= 9:
for i, act in enumerate(acts):
state = state[:act.data] + str(i+1) + state[act.data+1:]
s = []
@ -128,11 +130,10 @@ class TTTState(State):
elif b == 'O':
return -1.0 + 2.0 * self.curPlayer
else:
return 1.0 - 2.0 * self.curPlayer
return 1.0 - 2.0 * self.curPlayer
def getTensor(self, player=None, phase='default'):
if player==None:
if player == None:
player = self.curPlayer
s = ''
for row in range(1, 10):
@ -144,51 +145,60 @@ class TTTState(State):
def getModel(cls, phase='default'):
return Model()
class Model(nn.Module):
def __init__(self):
super().__init__()
self.chansPerSmol = 24
self.chansPerSlot = 8
self.chansComp = 8
self.smol = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=16,
kernel_size=(3,3),
out_channels=self.chansPerSmol,
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.comb = nn.Sequential(
nn.Conv1d(
in_channels=self.chansPerSmol,
out_channels=self.chansPerSlot,
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(16*9, 12),
nn.Linear(self.chansPerSlot*9, self.chansComp),
nn.ReLU(),
nn.Linear(12, 1),
nn.Linear(self.chansComp, 1),
#nn.Linear(9*8, 32),
# nn.ReLU(),
#nn.Linear(32, 8),
# nn.ReLU(),
#nn.Linear(16*9, 12),
# nn.ReLU(),
#nn.Linear(12, 1),
nn.Sigmoid()
)
def forward(self, x):
x = torch.reshape(x, (1,9,9))
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, (self.chansPerSmol, 9))
x = self.comb(x)
x = torch.reshape(x, (-1,))
y = self.out(x)
return y
def humanVsAi(train=True, remember=False, depth=3, bots=[0,1], noBg=False):
def humanVsAi(train=True, remember=False, depth=3, bots=[0, 1], noBg=False):
init = TTTState()
run = NeuralRuntime(init)
run.game(bots, depth, bg=not noBg)
@ -199,27 +209,31 @@ def humanVsAi(train=True, remember=False, depth=3, bots=[0,1], noBg=False):
trainer.saveToMemoryBank(run.head)
print('[!] Your cognitive and strategic destinctiveness was added to my own! (Game inserted into memoryBank)')
if train:
print("[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
print(
"[!] Your knowledge will be assimilated!!! Please stand by.... (Updating Neuristic)")
trainer.trainFromTerm(run.head)
print('[!] I have become smart! Destroyer of human Ultimate-TicTacToe players! (Neuristic update completed)')
print('[!] This marks the beginning of the end of humankind!')
print('[i] Thanks for playing! Goodbye...')
def aiVsAiLoop():
init = TTTState()
trainer = Trainer(init)
trainer.train()
if __name__=='__main__':
options = ['Play Against AI','Play Against AI (AI begins)','Play Against AI (Fast Play)','Playground','Let AI train']
if __name__ == '__main__':
options = ['Play Against AI',
'Play Against AI (AI begins)', 'Play Against AI (Fast Play)', 'Playground', 'Let AI train']
opt = choose('?', options)
if opt == options[0]:
humanVsAi()
elif opt == options[1]:
humanVsAi(bots[1,0])
humanVsAi(bots[1, 0])
elif opt == options[2]:
humanVsAi(depth=2,noBg=True)
humanVsAi(depth=2, noBg=True)
elif opt == options[3]:
humanVsAi(bots=[None,None])
humanVsAi(bots=[None, None])
else:
aiVsAiLoop()