def evaluateLeaf(self, leaf, value, done, breadcrumbs):
lg.logger_mcts.info('------EVALUATING LEAF------')
if done == 0:
value, probs, allowedActions = self.get_preds(leaf.state)
lg.logger_mcts.info('PREDICTED VALUE FOR %d: %f',
leaf.state.playerTurn, value)
probs = probs[allowedActions]
for idx, action in enumerate(allowedActions):
newState, _, _ = leaf.state.takeAction(action)
if newState.id not in self.mcts.tree:
node = mc.Node(newState)
self.mcts.addNode(node)
lg.logger_mcts.info('added node...%s...p = %f', node.id,
probs[idx])
else:
node = self.mcts.tree[newState.id]
lg.logger_mcts.info('existing node...%s...', node.id)
newEdge = mc.Edge(leaf, node, probs[idx], action)
leaf.edges.append((action, newEdge))
else:
lg.logger_mcts.info('GAME VALUE FOR %d: %f', leaf.playerTurn,
value)
return ((value, breadcrumbs))
def evaluateLeaf(self, leaf, value, done, breadcrumbs):
lg.logger_mcts.debug('------EVALUATING LEAF------')
if not done:
state = GameState.from_id(leaf.state_id, config.GRID_SHAPE)
value, probs, allowedActions = self.get_preds(state)
lg.logger_mcts.debug('PREDICTED VALUE FOR %d: %f',
state.currentPlayer, value)
for idx, allowedAction in enumerate(allowedActions):
if allowedAction:
newState, _, _ = state.takeAction(idx)
if newState.id not in self.mcts.tree:
node = mc.Node(newState)
self.mcts.addNode(node)
lg.logger_mcts.debug('added node...%s...p = %f',
node.state_id, probs[idx])
else:
node = self.mcts.tree[newState.id]
lg.logger_mcts.debug('existing node...%s...',
node.state_id)
newEdge = mc.Edge(leaf, node, probs[idx], idx)
leaf.edges.append((idx, newEdge))
else:
lg.logger_mcts.debug(
'GAME VALUE FOR %d: %f',
GameState.current_player_from_id(leaf.state_id), value)
return ((value, breadcrumbs))
def evaluateLeaf(self, leaf, value, done, breadcrumbs):
if done == 0:
value, probs, allowedActions = self.get_preds(leaf.state)
probs = probs[allowedActions]
for idx, action in enumerate(allowedActions):
newState, _, _ = leaf.state.takeAction(index_to_move(action))
if newState.id not in self.mcts.tree:
node = mc.Node(newState)
self.mcts.addNode(node)
else:
node = self.mcts.tree[newState.id]
newEdge = mc.Edge(leaf, node, probs[idx], action)
leaf.edges.append((action, newEdge))
return ((value, breadcrumbs))
def evaluateLeaf(self, leaf, value, done, breadcrumbs):
lg.logger_mcts.info('------EVALUATING LEAF------')
#评估该叶子节点，如果不是终态，则进行预测
if done == 0:
#经过预测得到的value值已经不是0或者-1了，因为对于非终态（目前棋盘上的棋子暂时无法判断胜负）
# 的叶子节点返回的价值，无法决定整个棋盘的局势，需要通过神经网络来预测
value, probs, allowedActions = self.get_preds(leaf.state)
lg.logger_mcts.info('PREDICTED VALUE FOR %d: %f',
leaf.state.playerTurn, value)
#
probs = probs[allowedActions]
for idx, action in enumerate(allowedActions):
# takeAction return (newState, value, done)
newState, _, _ = leaf.state.takeAction(action)
if newState.id not in self.mcts.tree:
#获取节点的信息
#节点的id其实是用棋盘黑白棋的状态合并来表示的
#Node id：000000000000000000000000000000000001000000000000000000000000000000000010000000000000
#node本身是一个包含了众多信息的“类”
node = mc.Node(newState)
#添加到树中
self.mcts.addNode(node)
lg.logger_mcts.info('added node...%s...p = %f', node.id,
probs[idx])
else:
#tree相当于一个数组，id是索引，node是值，用来存储当前的树
node = self.mcts.tree[newState.id]
lg.logger_mcts.info('existing node...%s...', node.id)
#Edge： inNode, outNode, prior, action
#inNode是currentNode，outNode是采取行动后新到达的节点
#传入了 ['P'] 值
newEdge = mc.Edge(leaf, node, probs[idx], action)
leaf.edges.append((action, newEdge))
else:
#如果叶子节点即为终态，则直接返回价值0或者-1
lg.logger_mcts.info('GAME VALUE FOR %d: %f', leaf.playerTurn,
value)
#返回当前行棋序列下（棋盘）的价值
return ((value, breadcrumbs))
def evaluate_leaf(self, leaf, value, over, backtrack):
if over == 0:
value, probabilities, allowed = self.get_predictions(leaf.state)
# maybe trying to only have probs of allowed moves?
probs = []
for move in allowed:
probs.append(probabilities[move])
for idx, move in enumerate(allowed):
new_state, _, _ = leaf.state.make_move(move)
if new_state.id not in self.mcts.tree:
node = mc.Node(new_state)
self.mcts.add_node(node)
else:
node = self.mcts.tree[new_state.id]
new_edge = mc.Edge(leaf, node, probs[idx], move)
leaf.edges.append((move, new_edge))
return (value, backtrack)
