dodox/vacuumDecay
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Smashed bugs

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This commit is contained in:
Dominik Moritz Roth 2022-04-15 18:11:18 +02:00
parent c4ff7832ef
commit bc64d679bf
3 changed files with 27 additions and 9 deletions
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brains/uttt.pth
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5
ultimatetictactoe.py
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@ -93,9 +93,14 @@ class TTTState(State):
self.update_box_won() self.update_box_won()
game_won = self.check_small_box(self.box_won) game_won = self.check_small_box(self.box_won)
if game_won == '.': if game_won == '.':
if self.checkDraw():
return -1
return None return None
return game_won == 'X' return game_won == 'X'
def checkDraw(self):
return len(self.getAvaibleActions())==0
def __str__(self): def __str__(self):
state = self.board state = self.board
acts = list(self.getAvaibleActions()) acts = list(self.getAvaibleActions())

31
vacuumDecay.py
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@ -187,8 +187,6 @@ class Node():
def _expand(self): def _expand(self):
self._childs = [] self._childs = []
if self.getWinner()!=None:
return
actions = self.state.getAvaibleActions() actions = self.state.getAvaibleActions()
for action in actions: for action in actions:
newNode = Node(self.state.mutate(action), self.universe, self, action) newNode = Node(self.state.mutate(action), self.universe, self, action)
@ -287,12 +285,12 @@ class Node():
self._calcScore(p) self._calcScore(p)
def _calcScore(self, player): def _calcScore(self, player):
winner = self.getWinner() winner = self._getWinner()
if winner!=None: if winner!=None:
if winner==-1: if winner==player:
self._scores[player] = 2/3 # draw
elif winner==player:
self._scores[player] = 0.0 self._scores[player] = 0.0
elif winner==-1:
self._scores[player] = 2/3
else: else:
self._scores[player] = 1.0 self._scores[player] = 1.0
return return
@ -321,17 +319,20 @@ class Node():
def curPlayer(self): def curPlayer(self):
return self.state.curPlayer return self.state.curPlayer
def _getWinner(self):
return self.state.checkWin()
def getWinner(self): def getWinner(self):
if len(self.childs)==0: if len(self.childs)==0:
return -1 return -1
return self.state.checkWin() return self._getWinner()
def _activateEdge(self, dist=0): def _activateEdge(self, dist=0):
if not self.strongScoresAvaible(): if not self.strongScoresAvaible():
self.universe.newOpen(self) self.universe.newOpen(self)
else: else:
for c in self.childs: for c in self.childs:
if c._cascadeMemory > 0.001*dist or random.random()<0.01: if c._cascadeMemory > 0.001*(dist-2) or random.random()<0.01:
c._activateEdge(dist=dist+1) c._activateEdge(dist=dist+1)
def __str__(self): def __str__(self):
@ -463,6 +464,7 @@ class NeuralRuntime(Runtime):
class Trainer(Runtime): class Trainer(Runtime):
def __init__(self, initState): def __init__(self, initState):
self.universe = Universe() self.universe = Universe()
#self.universe = QueueingUniverse()
self.rootNode = Node(initState, universe = self.universe) self.rootNode = Node(initState, universe = self.universe)
self.terminal = None self.terminal = None
@ -470,10 +472,12 @@ class Trainer(Runtime):
print('[*] Building Timeline') print('[*] Building Timeline')
term = self.linearPlay(model, calcDepth=depth, exacity=exacity) term = self.linearPlay(model, calcDepth=depth, exacity=exacity)
if refining: if refining:
print('[*] Refining Timeline') print('[*] Refining Timeline (exploring alternative endings)')
self.fanOut(term, depth=depth+2) self.fanOut(term, depth=depth+2)
self.fanOut(term.parent, depth=depth+2) self.fanOut(term.parent, depth=depth+2)
self.fanOut(term.parent.parent, depth=depth+2) self.fanOut(term.parent.parent, depth=depth+2)
#print('[*] Refining Timeline (exploring uncertain regions)')
#self.timelineExpandUncertain(term, 20)
return term return term
def fanOut(self, head, depth=4): def fanOut(self, head, depth=4):
@ -514,6 +518,15 @@ class Trainer(Runtime):
return return
head = head.parent head = head.parent
def timelineExpandUncertain(self, term, secs):
return
self.rootNode.universe.clearPQ()
self.rootNode.universe.activateEdge(rootNode)
self.spawnWorker()
time.sleep(secs)
self.rootNode.universe.clearPQ()
self.killWorker()
def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, term=None): def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, term=None):
loss_func = nn.MSELoss() loss_func = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr) optimizer = optim.Adam(model.parameters(), lr)