class MCTSPlayer:
def __init__(self,
nPlay,
maxPlies,
bNegamax,
cUct=1 / np.sqrt(2),
bDump=False):
self._nPlay = nPlay
self._maxPlies = maxPlies
if bNegamax:
self._uct = UCTNegamax(cUct)
else:
self._uct = UCT(cUct)
self._cUct = cUct
self._bNegamax = bNegamax
self._bDump = bDump
self._uctMove = UCT(0)
self._rp = RandomPlayer()
self._nprand = np.random.RandomState()
self._root = None
def __str__(self):
return ("%s nPlay = %d maxPlies = %d bNegamax = %s cUct = %.4f" %
(self.__class__.__name__, self._nPlay, self._maxPlies,
self._bNegamax, self._cUct))
def _simulate(self, node):
# "A simulation is run from the new node(s) according to the
# default policy to produce an outcome."
return play.playRest(self._rp, self._rp, node.ttt.clone(), False,
99999)[0]
def setSeed(self, seed):
self._nprand.seed(seed)
self._rp.setSeed(seed + 1)
def move(self, ttt):
if self._root is not None:
self._root = self._root.findBoard(ttt)
if self._root is None:
self._root = Node(self._nprand, ttt, 1, maxPlies=self._maxPlies)
marker = ttt.whoseTurn()
for _ in range(self._nPlay):
nodeLeaf = self._root.select(self._uct)
if nodeLeaf is not None:
nodeSim = nodeLeaf.expand()
if nodeSim is not None:
# print ("START:", nodeSim.maxPlies, nodeSim.move)
w = self._simulate(nodeSim)
if w == ttt.whoseTurn():
score = 1
elif w == game.Draw:
score = .5
else:
score = 0
# print ("SCORE:", marker, w, score)
nodeSim.backpropagate(score)
if self._bDump:
self._root.dump()
self._root = self._root.bestChild(self._uctMove)
return self._root.move
def tests(self):
self._root.check_parentage()
class OmniscientAdversary:
def __init__(self, nPlay):
self._rp = RandomPlayer()
self._rand = random.Random()
self._epsSame = 1e-6
self._nPlay = nPlay
def __str__(self):
return "%s nPlay = %d" % (self.__class__.__name__, self._nPlay)
def reconfigure(self, nn):
self._nn = nn
def setSeed(self, seed):
if seed is None:
self._rp.setSeed(None)
self._rand.seed(None)
else:
self._rp.setSeed(seed)
self._rand.seed(seed+1)
def move(self, ttt):
bestQ = -1e99
qs = []
vm = ttt.validMoves()
for m in vm:
q = self._moveQuality(ttt, m)
if q > bestQ:
bestQ = q
qs.append(q)
bestMoves = []
for iMove, q in enumerate(qs):
if abs(q-bestQ) < self._epsSame:
bestMoves.append(vm[iMove])
return random.choice(bestMoves)
def xx_move(self, ttt):
bestQ = -1e99
qs = []
vm = ttt.validMoves()
for m in vm:
q = self._moveQuality(ttt, m)
if q > bestQ:
bestQ = q
qs.append(q)
qs = np.array(qs)
pMove = qs - qs.min() + 1e-6
pMove /= pMove.sum()
return np.random.choice(vm, p=pMove)
def _moveQuality(self, ttt, m):
scores = []
if ttt.whoseTurn() == game.X:
pX = self._rp
pO = self._nn
else:
pX = self._nn
pO = self._rp
nPlay = self._nPlay
for _ in range(nPlay):
scores.append(play.simGame(pX, pO, ttt, m))
scores = np.array(scores)
return scores.mean()
