Python 执行时间问题

在开发一个基于 Python 的游戏 AI 时，我们遇到了一个执行时间问题。游戏的目标是让一个代理找到从起点到终点的最短路径。该代理使用了一个搜索算法来计算路径。在第一个函数 registerInitialState 中，我们计算出路径并存储在本地变量 self.actions 中。在第二个函数 getAction 中，我们从 self.actions 中获取下一个动作。

def registerInitialState(self, state):
    """
    This is the first time that the agent sees the layout of the game board. Here, we
    choose a path to the goal.  In this phase, the agent should compute the path to the
    goal and store it in a local variable.  All of the work is done in this method!

    state: a GameState object (pacman.py)
    """
    if self.searchFunction == None: raise Exception, "No search function provided for SearchAgent"
    starttime = time.time()
    problem = self.searchType(state) # Makes a new search problem
    self.actions  = self.searchFunction(problem) # Find a path
    totalCost = problem.getCostOfActions(self.actions)
    print('Path found with total cost of %d in %.1f seconds' % (totalCost, time.time() - starttime))
    if '_expanded' in dir(problem): print('Search nodes expanded: %d' % problem._expanded)

def getAction(self, state):
    """
    Returns the next action in the path chosen earlier (in registerInitialState).  Return
    Directions.STOP if there is no further action to take.

    state: a GameState object (pacman.py)
    """
    if 'actionIndex' not in dir(self): self.actionIndex = 0
    i = self.actionIndex
    self.actionIndex += 1
    if i < len(self.actions):
      return self.actions[i]    
    else:
      return Directions.STOP

然而，当我们运行这个程序时，我们发现 getAction 函数没有执行，并且抛出了一个错误：“TypeError: len() of unsized object”。

2、解决方案

为了解决这个问题，我们对代码进行了调试，并发现了问题所在。在 getAction 函数中，我们使用 len(self.actions) 来检查 self.actions 的长度，但是此时 self.actions 为 None，因此导致了错误。

问题的根源在于 registerInitialState 函数中。在该函数中，我们调用了 self.searchFunction(problem) 来计算路径，并将结果存储在 self.actions 中。然而，如果 self.searchFunction 函数没有找到路径，则会返回 None。

为了解决这个问题，我们在 registerInitialState 函数中添加了一个检查，如果 self.searchFunction 返回 None，则将 self.actions 设置为 []。这样，在 getAction 函数中，我们就不会再遇到 TypeError 错误了。

def registerInitialState(self, state):
    """
    This is the first time that the agent sees the layout of the game board. Here, we
    choose a path to the goal.  In this phase, the agent should compute the path to the
    goal and store it in a local variable.  All of the work is done in this method!

    state: a GameState object (pacman.py)
    """
    if self.searchFunction == None: raise Exception, "No search function provided for SearchAgent"
    starttime = time.time()
    problem = self.searchType(state) # Makes a new search problem
    self.actions  = self.searchFunction(problem) # Find a path
    if self.actions == None: self.actions = [] # Fix the problem
    totalCost = problem.getCostOfActions(self.actions)
    print('Path found with total cost of %d in %.1f seconds' % (totalCost, time.time() - starttime))
    if '_expanded' in dir(problem): print('Search nodes expanded: %d' % problem._expanded)

def getAction(self, state):
    """
    Returns the next action in the path chosen earlier (in registerInitialState).  Return
    Directions.STOP if there is no further action to take.

    state: a GameState object (pacman.py)
    """
    if 'actionIndex' not in dir(self): self.actionIndex = 0
    i = self.actionIndex
    self.actionIndex += 1
    if i < len(self.actions):
      return self.actions[i]    
    else:
      return Directions.STOP

经过修改后，我们的程序能够正确地运行，并且能够找到从起点到终点的最短路径。