public class obstacles : MonoBehaviour { GameObject overUI; // Start is called before the first frame update void Start() { overUI = GameObject.Find("over"); overUI.SetActive(false); } // Update is called once per frame void Update() { } private void OnCollisionEnter(Collision collision) { if (collision.gameObject.name == "player") { //Debug.Log("碰到了Cube (2)"); //Destroy(collision.gameObject); Debug.Log("失败"); overUI.SetActive(true); Time.timeScale = 0; } } }这个脚本反在多个物体上运行显示NullReferenceException: Object reference not set to an instance of an object obstacles.Start () (at Assets/0419/0422/obstacles.cs:12)

很抱歉，我不确定您的问题是什么。这段代码可能会引发NullReferenceException错误，因为如果游戏物体“over”在场景中不存在，那么GameObject.Find("over")将返回null，从而导致overUI变量未实例化。如果您能提供更多的上下文或信息，我可以更好地回答您的问题。

相关问题

class WorldEnv: def __init__(self): self.distance_threshold = 0.01 self.action_bound = 1 self.goal = None self.state = None self.path = [] self.success_rate = [] self.obstacles = [((2, 2), (3, 3)), ((0, 4), (3, 5)), ((4, 1), (5, 4))] self.obstacle_margin = 0.3 def reset(self): self.goal = np.array([5, 5]) self.state = np.array([1, 1], dtype=np.float64) self.start = np.array([1, 1]) self.count = 0 self.path = [self.state.tolist()] return np.hstack((self.state, self.goal)) def step(self, action): action = np.clip(action, -self.action_bound, self.action_bound) x = max(0, min(5, self.state[0] + action[0])) y = max(0, min(5, self.state[1] + action[1])) self.state = np.array([x, y]) self.count += 1 dis = np.sqrt(np.sum(np.square(self.state - self.goal))) reward = -1.0 if dis > self.distance_threshold else 0 if dis <= self.distance_threshold or self.count == 50: done = True else: done = False return np.hstack((self.state, self.goal)), reward, done 修改代码，让智能体如果下一步动作后距离障碍物的边界或地图边界小于0.3，或处于障碍物中，或动作序列超过50，奖励-1，结束动作序列，返回初始状态。如果智能体到达目标或距离目标小于0.01，奖励1，结束动作，返回初始状态

修改后的代码如下：

import numpy as np

class WorldEnv:
    def __init__(self):
        self.distance_threshold = 0.01
        self.action_bound = 1
        self.goal = None
        self.state = None
        self.path = []
        self.success_rate = []
        self.obstacles = [((2, 2), (3, 3)), ((0, 4), (3, 5)), ((4, 1), (5, 4))]
        self.obstacle_margin = 0.3

    def reset(self):
        self.goal = np.array([5, 5])
        self.state = np.array([1, 1], dtype=np.float64)
        self.start = np.array([1, 1])
        self.count = 0
        self.path = [self.state.tolist()]
        return np.hstack((self.state, self.goal))

    def step(self, action):
        action = np.clip(action, -self.action_bound, self.action_bound)
        x = max(0, min(5, self.state[0] + action[0]))
        y = max(0, min(5, self.state[1] + action[1]))
        
        # Check if the next step is close to an obstacle or outside the map boundaries
        next_pos = np.array([x, y])
        if self.is_close_to_obstacle(next_pos) or self.is_outside_boundary(next_pos):
            reward = -1.0
            done = True
            return np.hstack((self.state, self.goal)), reward, done
        
        self.state = next_pos
        self.count += 1

        dis = np.sqrt(np.sum(np.square(self.state - self.goal)))
        if dis <= self.distance_threshold:
            reward = 1.0
            done = True
        elif self.count == 50:
            reward = -1.0
            done = True
        else:
            reward = 0.0
            done = False

        return np.hstack((self.state, self.goal)), reward, done
    
    def is_close_to_obstacle(self, pos):
        for obstacle in self.obstacles:
            if self.calculate_distance_to_obstacle(pos, obstacle) < self.obstacle_margin:
                return True
        return False
    
    def calculate_distance_to_obstacle(self, pos, obstacle):
        x_min, y_min = obstacle[0]
        x_max, y_max = obstacle[1]
        dx = max(0, max(x_min - pos[0], pos[0] - x_max))
        dy = max(0, max(y_min - pos[1], pos[1] - y_max))
        return np.sqrt(dx**2 + dy**2)
    
    def is_outside_boundary(self, pos):
        return pos[0] < 0 or pos[0] > 5 or pos[1] < 0 or pos[1] > 5

在 `step` 方法中，我添加了两个新的方法 `is_close_to_obstacle` 和 `calculate_distance_to_obstacle`，用于检查智能体是否靠近障碍物以及计算智能体到障碍物的距离。如果下一步的位置满足靠近障碍物或超出地图边界的条件，那么奖励将设为-1，并且动作序列将结束，返回初始状态。

另外，我还添加了 `is_outside_boundary` 方法，用于判断智能体是否超出地图边界。

请注意，这只是一种修改方案，具体如何修改代码取决于您的需求和设计。

修改以下代码，使他避开障碍物：def Astar(Map): s = Point(Map.start, None, None) openlist = [s] closedlist = [] s.get_f(Map.goal) while openlist: n = openlist.pop(0) if n.position[0] == Map.goal[0] and n.position[1] == Map.goal[1]: return n else: closedlist.append(n) subp_list = Point.EXPAND(n, Map) for subp in subp_list: subp.get_f(Map.goal) for openp in openlist: if openp.position[0] == subp.position[0] and openp.position[1] == subp.position[1]: if subp.f < openp.f: openlist.remove(openp) openlist.append(subp) for closedp in closedlist: if closedp.position[0] == subp.position[0] and closedp.position[1] == subp.position[1]: if subp.f < closedp.f: closedlist.remove(closedp) openlist.append(subp) openlist.append(subp) openlist.sort(key= lambda x:x.f)

Sure, here's the modified code that avoids obstacles using A* algorithm:

def Astar(Map):
    s = Point(Map.start, None, None)
    openlist = [s]
    closedlist = []
    s.get_f(Map.goal)
    while openlist:
        n = openlist.pop(0)
        if n.position[0] == Map.goal[0] and n.position[1] == Map.goal[1]:
            return n
        else:
            closedlist.append(n)
            subp_list = Point.EXPAND(n, Map)
            for subp in subp_list:
                if Map.is_obstacle(subp.position[0], subp.position[1]):
                    continue
                subp.get_f(Map.goal)
                for openp in openlist:
                    if openp.position[0] == subp.position[0] and openp.position[1] == subp.position[1]:
                        if subp.f < openp.f:
                            openlist.remove(openp)
                            openlist.append(subp)
                            break
                else:
                    for closedp in closedlist:
                        if closedp.position[0] == subp.position[0] and closedp.position[1] == subp.position[1]:
                            if subp.f < closedp.f:
                                closedlist.remove(closedp)
                                openlist.append(subp)
                                break
                    else:
                        openlist.append(subp)
            openlist.sort(key=lambda x: x.f)

The modification is done by adding a check for obstacles in the sub-points list and skipping those points, as well as updating the open and closed lists based on the new condition.