Wider timeline iter & beautify
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parent
6967243ae2
commit
4a018638d5
136
vacuumDecay.py
136
vacuumDecay.py
@ -1,3 +1,7 @@
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if __name__ == '__main__':
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print('[!] VacuumDecay should not be started directly')
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exit()
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import os
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import io
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import time
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@ -17,6 +21,7 @@ import random
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import datetime
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import pickle
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class Action():
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# Should hold the data representing an action
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# Actions are applied to a State in State.mutate
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@ -37,6 +42,7 @@ class Action():
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# should start with < and end with >
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return "<P"+str(self.player)+"-"+str(self.data)+">"
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class State(ABC):
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# Hold a representation of the current game-state
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# Allows retriving avaible actions (getAvaibleActions) and applying them (mutate)
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@ -110,6 +116,7 @@ class State(ABC):
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def getScoreNeural(self, model, player=None, phase='default'):
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return model(self.getTensor(player=player, phase=phase)).item()
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class Universe():
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def __init__(self):
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self.scoreProvider = 'naive'
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@ -129,11 +136,13 @@ class Universe():
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def activateEdge(self, head):
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pass
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@dataclass(order=True)
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class PQItem:
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priority: int
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data: Any = field(compare=False)
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class QueueingUniverse(Universe):
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def __init__(self):
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super().__init__()
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@ -193,7 +202,8 @@ class Node():
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self._childs = []
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actions = self.state.getAvaibleActions()
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for action in actions:
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newNode = Node(self.state.mutate(action), self.universe, self, action)
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newNode = Node(self.state.mutate(action),
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self.universe, self, action)
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self._childs.append(self.universe.merge(newNode))
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def getStrongFor(self, player):
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@ -213,11 +223,13 @@ class Node():
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best = c.getStrongFor(p)
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strongs[p] = best
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else:
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scos = [(c.getStrongFor(p), c.getStrongFor(cp)) for c in self.childs]
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scos = [(c.getStrongFor(p), c.getStrongFor(cp))
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for c in self.childs]
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scos.sort(key=lambda x: x[1])
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betterHalf = scos[:max(3, int(len(scos)/3))]
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myScores = [bh[0]**2 for bh in betterHalf]
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strongs[p] = sqrt(myScores[0]*0.75 + sum(myScores)/(len(myScores)*4))
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strongs[p] = sqrt(myScores[0]*0.75 +
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sum(myScores)/(len(myScores)*4))
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update = False
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for s in range(self.playersNum):
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if strongs[s] != self._strongs[s]:
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@ -301,7 +313,8 @@ class Node():
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if self.universe.scoreProvider == 'naive':
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self._scores[player] = self.state.getScoreFor(player)
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elif self.universe.scoreProvider == 'neural':
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self._scores[player] = self.state.getScoreNeural(self.universe.model, player)
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self._scores[player] = self.state.getScoreNeural(
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self.universe.model, player)
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else:
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raise Exception('Uknown Score-Provider')
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@ -350,6 +363,7 @@ class Node():
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s.append("[ score: "+str(self.getScoreFor(0))+" ]")
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return '\n'.join(s)
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def choose(txt, options):
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while True:
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print('[*] '+txt)
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@ -371,6 +385,7 @@ def choose(txt, options):
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return opt
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print('[!] Invalid Input.')
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class Worker():
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def __init__(self, universe):
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self.universe = universe
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@ -396,6 +411,7 @@ class Worker():
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def revive(self):
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self._alive = True
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class Runtime():
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def __init__(self, initState):
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universe = QueueingUniverse()
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@ -458,17 +474,42 @@ class Runtime():
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if bg:
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self.killWorker()
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def saveModel(self, model, gen):
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dat = model.state_dict()
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with open(self.getModelFileName(), 'wb') as f:
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pickle.dump((gen, dat), f)
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def loadModelState(self, model):
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with open(self.getModelFileName(), 'rb') as f:
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gen, dat = pickle.load(f)
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model.load_state_dict(dat)
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model.eval()
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return gen
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def loadModel(self):
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model = self.head.state.getModel()
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gen = self.loadModelState(model)
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return model, gen
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def getModelFileName(self):
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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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with open('memoryBank/uttt/'+datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')+'_'+str(int(random.random()*99999))+'.vdm', 'wb') as f:
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pickle.dump(term, f)
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class NeuralRuntime(Runtime):
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def __init__(self, initState):
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super().__init__(initState)
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model = self.head.state.getModel()
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model.load_state_dict(torch.load('brains/uttt.pth'))
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model.eval()
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model, gen = self.loadModel()
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self.head.universe.model = model
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self.head.universe.scoreProvider = 'neural'
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class Trainer(Runtime):
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def __init__(self, initState):
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super().__init__(initState)
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@ -523,15 +564,35 @@ class Trainer(Runtime):
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print(' => '+['O', 'X', 'No one'][head.getWinner()] + ' won!')
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return head
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def timelineIter(self, term):
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head = term
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def timelineIterSingle(self, term):
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for i in self.timelineIter(self, [term]):
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yield i
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def timelineIter(self, terms, altChildPerNode=-1):
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batch = len(terms)
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heads = terms
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while True:
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empty = True
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for b in range(batch):
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head = heads[b]
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if head == None:
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continue
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empty = False
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yield head
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if len(head.childs):
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if altChildPerNode == -1: # all
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for child in head.childs:
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yield child
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else:
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for j in range(min(altChildPerNode, int(len(head.childs)/2))):
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yield random.choice(head.childs)
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if head.parent == None:
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return
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head = None
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else:
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head = head.parent
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heads[b] = head
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if empty:
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return
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def timelineExpandUncertain(self, term, secs):
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self.rootNode.universe.clearPQ()
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@ -544,20 +605,24 @@ class Trainer(Runtime):
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self.killWorker()
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print('')
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def trainModel(self, model, lr=0.00005, cut=0.01, calcDepth=4, exacity=5, term=None):
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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 term==None:
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term = self.buildDatasetFromModel(model, depth=calcDepth, exacity=exacity)
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if terms == None:
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terms = []
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for i in range(batch):
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terms.append(self.buildDatasetFromModel(
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model, depth=calcDepth, exacity=exacity))
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print('[*] Conditioning Brain')
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for r in range(64):
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loss_sum = 0
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lLoss = 0
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zeroLen = 0
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for i, node in enumerate(self.timelineIter(term)):
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for i, node in enumerate(self.timelineIter(terms)):
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for p in range(self.rootNode.playersNum):
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inp = node.state.getTensor(player=p)
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gol = torch.tensor([node.getStrongFor(p)], dtype=torch.float)
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gol = torch.tensor(
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[node.getStrongFor(p)], dtype=torch.float)
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out = model(inp)
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loss = loss_func(out, gol)
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optimizer.zero_grad()
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@ -584,27 +649,17 @@ class Trainer(Runtime):
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model.train()
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for gen in range(startGen, startGen+gens):
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print('[#####] Gen '+str(gen)+' training:')
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loss = self.trainModel(model, calcDepth=min(4,3+int(gen/16)), exacity=int(gen/3+1))
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loss = self.trainModel(model, calcDepth=min(
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4, 3+int(gen/16)), exacity=int(gen/3+1), batch=4)
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print('[L] '+str(loss))
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self.universe.scoreProvider = 'neural'
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self.saveModel(model, gen)
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def saveModel(self, model, gen):
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dat = model.state_dict()
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with open(self.getModelFileName(), 'wb') as f:
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pickle.dump((gen, dat), f)
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def loadModelState(self, model):
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with open(self.getModelFileName(), 'rb') as f:
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gen, dat = pickle.load(f)
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model.load_state_dict(dat)
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model.eval()
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return gen
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def loadModel(self):
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model = self.rootNode.state.getModel()
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gen = self.loadModelState(model)
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return model, gen
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def trainFromTerm(self, term):
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model, gen = self.loadModel()
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self.universe.scoreProvider = 'neural'
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self.trainModel(model, calcDepth=4, exacity=10, term=term)
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self.saveModel(model)
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def train(self):
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if os.path.exists(self.getModelFileName()):
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@ -612,20 +667,3 @@ class Trainer(Runtime):
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self.main(model, startGen=gen+1)
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else:
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self.main()
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def getModelFileName(self):
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return 'brains/utt.vac'
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def trainFromTerm(self, term):
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model = self.rootNode.state.getModel()
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model.load_state_dict(torch.load('brains/uttt.vac'))
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model.eval()
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self.universe.scoreProvider = 'neural'
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self.trainModel(model, calcDepth=4, exacity=10, term=term)
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self.saveModel(model)
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def saveToMemoryBank(self, term):
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return
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with open('memoryBank/uttt/'+datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S')+'_'+str(int(random.random()*99999))+'.vdm', 'wb') as f:
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pickle.dump(term, f)
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