def __init__(self):
self.start = time.time()
super().__init__(0, 0x0000000810000000, 0x0000001008000000)
self.model = Model()
#self.model.load('model/Gen' + str(0))
self.score = 0
self.table = {}
self.currentNode = mcts.Node(mcts.FakeNode(), 0, 0, Core(self.turn, self.black, self.white))
self.currentNode.isGameRoot = True
self.currentNode.isSearchRoot = True
self.mctsBatch = mcts.MCTSBatch(self.model,NUM_MCTS)
def InputEnemy(self):
nb, nw = self.Count()
nmTurn = 64 - nb - nw
if nmTurn < 16:
site, socre, self.table = moveAI(self.black,self.white,self.turn, nmTurn, self.table, self.score)
self.score = socre
self.AddSite(site)
else:
# site, score, self.table = moveAINN(self.black,self.white,self.turn, 3, self.table, self.score,self.model)
# self.score = score
# self.AddSite(site)
self.currentNode = mcts.Node(mcts.FakeNode(), 0, self.turn, Core(self.turn, self.black, self.white))
self.currentNode.isGameRoot = True
self.currentNode.isSearchRoot = True
pi = self.mctsBatch.alpha([self.currentNode], 1)[0]
print(pi)
self.currentNode = self.makeMove(self.currentNode,int(np.argmax(pi)))
def InputPlayer(self):
nb, nw = self.Count()
nmTurn = 64 - nb - nw
if nmTurn < RAND:
self.currentNode = mcts.Node(
mcts.FakeNode(), 0, self.turn,
Core(self.turn, self.black, self.white))
self.currentNode.isGameRoot = True
self.currentNode.isSearchRoot = True
pi = self.mctsBatch.alpha([self.currentNode], 1)[0]
move = int(np.argmax(pi))
self.addHistory(pi, self.turn)
self.currentNode = self.makeMove(self.currentNode, move)
else:
while True:
move = random.randrange(0, 64)
site = 1 << move
if self.judge & site:
# self.AddSite(site)
self.currentNode = self.makeMove(self.currentNode, move)
break
def InputEnemy(self):
nb, nw = self.Count()
nmTurn = 64 - nb - nw
if nmTurn < 16:
site, maxScore, sumScore, self.table = moveAITrain(
self.black, self.white, self.turn, nmTurn, self.table, nw - nb)
if maxScore > 0:
maxScore = 1
elif maxScore < 0:
maxScore = -1
site.sort(reverse=True)
pi = np.zeros(64)
for move in site:
pi[bitFind(move[1])] = move[0] / (sumScore if sumScore else 1)
self.addHistory(pi, self.turn)
temp = copy.deepcopy(self.history)
self.addValue(self.history, maxScore)
for i in range(len(site) // 4 + 1):
history2 = copy.deepcopy(temp)
self.AddSite(site[i][1])
self.NextBoard()
site2, maxScore2, sumScore2, self.table = moveAITrain(
self.black, self.white, self.turn ^ 1, nmTurn - 1,
self.table, nw - nb)
self.NextBoard()
maxScore2 = -maxScore2
if maxScore2 > 0:
maxScore2 = 1
elif maxScore2 < 0:
maxScore2 = -1
if site2:
site2.sort(reverse=True)
pi2 = np.zeros([64])
for move2 in site2:
pi2[bitFind(move2[1])] = move2[0] / (sumScore2 if
sumScore2 else 1)
features = []
features.append(self.black)
features.append(self.white)
features.append(self.judge)
history2.append([features, pi2, self.turn ^ 1])
self.addValue(history2, maxScore2)
self.history.extend(history2)
self.AddSite(0)
elif nmTurn < RAND:
self.currentNode = mcts.Node(
mcts.FakeNode(), 0, self.turn,
Core(self.turn, self.black, self.white))
self.currentNode.isGameRoot = True
self.currentNode.isSearchRoot = True
pi = self.mctsBatch.alpha([self.currentNode], 1)[0]
move = int(np.argmax(pi))
self.addHistory(pi, self.turn)
self.currentNode = self.makeMove(self.currentNode, move)
else:
while True:
move = random.randrange(0, 64)
site = 1 << move
if self.judge & site:
# self.AddSite(site)
self.currentNode = self.makeMove(self.currentNode, move)
break