Reward HDU - 2647(两种解法:dfs, 拓扑)

本文详细解析了邻接表的数据结构及其在图论问题中的应用,通过具体实例介绍了深度优先搜索(DFS)算法的实现过程,探讨了在算法实现中常见的错误,如死循环和计数错误,并提供了正确的解决方案。

 //在做的时候,邻接表构造错了,导致出现死循环问题tle。。。还有就是topo里面sum写在了if外,,,,导致计数错误WA

//dfs已解出

#include<iostream>
#include<cstdio>
#include <string.h>
#include<algorithm>
#include<queue>

using namespace std;

const int N = 20000+5;

struct Edge{
    int to;
    int next;
}e[N];

int vis[N];
int price[N];
int dgree[N];
int head[N];
int sum;        
int n, m;


//构造邻接表 
void add(int x, int y, int i)
{
    e[i].to = x;//逆向
    e[i].next = head[y];
    head[y] = i;
}


void dfs(int i)
{
    for(int j=head[i]; j != -1; j=e[j].next){

        int v = e[j].to;
        dgree[v]--;
        price[v] = max(price[v], price[i]+1);//每次都会更新,有一种可能就是之前存储的比现在多,就不用更新了.... 
        if(dgree[v] == 0 && vis[v] == 0){
            vis[v] = 1;
                    sum++;

            dfs(v);
        }
    }
    return;
}




void toposort()
{
	sum = 0;
	queue<int> q;
	
	for(int i=1; i<=n; i++){
		if(dgree[i] == 0){
			q.push(i);
			vis[i] = 1;
			sum ++;
		}
	}
		
		while(!q.empty()){
			int i = q.front();
			q.pop();
			
  for(int j=head[i]; j != -1; j=e[j].next){

        int v = e[j].to;
        dgree[v]--;
        
        price[v] = price[i]+1;

        if(dgree[v] == 0 && vis[v] == 0){

        	q.push(v);
            vis[v] = 1;
                    sum++;

        }
    }
		}
	
}


    int main()
    {

        while(~scanf("%d%d", &n, &m)){

            sum = 0;//为dfs做准备 
            memset(price, 0, sizeof(price));

            memset(vis, 0, sizeof(vis));
            memset(dgree, 0, sizeof(dgree));
            memset(head, -1, sizeof(head));

            for(int i=1; i<=m; i++){
                int a, b;
				scanf("%d%d", &a, &b); 
           //     cin >> a >> b;

                add(a, b, i);
                dgree[a]++;
            }

            for(int i=1; i<=n; i++){
                if(dgree[i] == 0 && vis[i] == 0){
sum++; 
                    vis[i] = 1;
                    dfs(i);
 

                }
            }
            
   //          					toposort();

  //          cout << sum << endl;
            if(sum == n){
                sum = n*888;

                for(int i=1; i<=n; i++){
                    sum += price[i];
                }
                printf("%d\n", sum);
           //     cout << sum << endl;
            }
            else printf("-1\n");


        }

        return 0;
    }

//ps:以下问题已解决。。。。详见上图第一个代码

//个人感觉可以用dfs解下,但是目前一直WA,待解决....

#include<iostream>
#include<cstdio>
#include <string.h>
#include<algorithm>
#include<queue>

using namespace std;

const int N = 20000+5;

struct Edge{
    int to;
    int next;
}e[N];

int vis[N];
int price[N];
int dgree[N];
int head[N];
int sum;        
int n, m;


//构造邻接表 
void add(int x, int y, int i)
{
    e[i].to = x;//逆向
    e[i].next = head[y];
    head[y] = i;
}
void dfs(int i)
{
    for(int j=head[i]; j != -1; j=e[j].next){

        int v = e[j].to;
        dgree[v]--;
        price[v] = price[i]+1;
        if(dgree[v] == 0 && vis[v] == 0){
            vis[v] = 1;
                    sum++;

            dfs(v);
        }
    }
}


void toposort()
{
	sum = 0;
	queue<int> q;
	
	for(int i=1; i<=n; i++){
		if(dgree[i] == 0){
			q.push(i);
			vis[i] = 1;
			sum ++;
		}
	}
		
		while(!q.empty()){
			int i = q.front();
			q.pop();
			
  for(int j=head[i]; j != -1; j=e[j].next){

        int v = e[j].to;
        dgree[v]--;
        
        price[v] = price[i]+1;

        if(dgree[v] == 0 && vis[v] == 0){

        	q.push(v);
            vis[v] = 1;
                    sum++;

        }
    }
		}
	
}


    int main()
    {

        while(~scanf("%d%d", &n, &m)){

            sum = 1;//为dfs做准备 
            memset(price, 0, sizeof(price));

            memset(vis, 0, sizeof(vis));
            memset(dgree, 0, sizeof(dgree));
            memset(head, -1, sizeof(head));

            for(int i=1; i<=m; i++){
                int a, b;
				scanf("%d%d", &a, &b); 
           //     cin >> a >> b;

                add(a, b, i);
                dgree[a]++;
            }

            for(int i=1; i<=n; i++){
                if(dgree[i] == 0){

                    vis[i] = 0;
                    dfs(i);
 

                }
            }
            
   //          					toposort();

  //          cout << sum << endl;
            if(sum == n){
                sum = n*888;

                for(int i=1; i<=n; i++){
                    sum += price[i];
                }
                printf("%d\n", sum);
           //     cout << sum << endl;
            }
            else printf("-1\n");


        }

        return 0;
    }

 

2025-08-28 08:34:14.948 | WARNING | PID:76327 | definition.reward_shaping:78 - aisrv Action count mismatch: 2 actions for 4 junctions. Truncating or padding actions. 2025-08-28 08:34:14.950 | WARNING | PID:76327 | definition.reward_shaping:171 - aisrv Converted list action to integer: 1 2025-08-28 08:34:14.951 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 0: No lanes for phase 1, using enter lanes 2025-08-28 08:34:14.952 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 0: name 'travel_reward' is not defined 2025-08-28 08:34:14.952 | WARNING | PID:76327 | definition.reward_shaping:171 - aisrv Converted list action to integer: 2 2025-08-28 08:34:14.953 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 1: No lanes for phase 2, using enter lanes 2025-08-28 08:34:14.954 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 1: name 'travel_reward' is not defined 2025-08-28 08:34:14.954 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 2: No lanes for phase 0, using enter lanes 2025-08-28 08:34:14.955 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 2: name 'travel_reward' is not defined 2025-08-28 08:34:14.956 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 3: No lanes for phase 0, using enter lanes 2025-08-28 08:34:14.957 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 3: name 'travel_reward' is not defined learner is alive aisrv is alive Error found in file: /data/projects/intelligent_traffic_lights_v2/log/aisrv/aisrv_kaiwu_rl_helper_pid76327_log_2025-08-28-08.log Error content (line 24): {"time": "2025-08-28 08:34:14.952136", "level": "ERROR", "message": "aisrv Error processing junction 0: name 'travel_reward' is not defined", "file": "definition.py", "line": "220", "module": "aisrv", "process": "definition", "function": "reward_shaping", "stack": "", "pid": 76327} 2025-08-28 08:34:18.131 | WARNING | PID:76327 | definition.reward_shaping:78 - aisrv Action count mismatch: 2 actions for 4 junctions. Truncating or padding actions. 2025-08-28 08:34:18.133 | WARNING | PID:76327 | definition.reward_shaping:171 - aisrv Converted list action to integer: 1 2025-08-28 08:34:18.133 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 0: No lanes for phase 1, using enter lanes 2025-08-28 08:34:18.134 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 0: name 'travel_reward' is not defined 2025-08-28 08:34:18.134 | WARNING | PID:76327 | definition.reward_shaping:171 - aisrv Converted list action to integer: 2 2025-08-28 08:34:18.135 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 1: No lanes for phase 2, using enter lanes 2025-08-28 08:34:18.135 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 1: name 'travel_reward' is not defined 2025-08-28 08:34:18.136 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 2: No lanes for phase 0, using enter lanes 2025-08-28 08:34:18.137 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 2: name 'travel_reward' is not defined 2025-08-28 08:34:18.137 | WARNING | PID:76327 | definition.reward_shaping:190 - aisrv Junction 3: No lanes for phase 0, using enter lanes 2025-08-28 08:34:18.138 | ERROR | PID:76327 | definition.reward_shaping:220 - aisrv Error processing junction 3: name 'travel_reward' is not defined 2025-08-28 08:34:18.140 | INFO | PID:76327 | train_workflow.workflow:98 - aisrv Avg Step Reward: 0.00, Epoch: 0, Data Length: 2
最新发布
08-29
<think>根据错误日志,问题在于`travel_reward`变量未定义。在奖励计算过程中,当处理每个路口时,我们调用了`calculate_travel_reward`函数来计算通行奖励,但是在更新历史指标时,我们使用了`travel_reward`变量,而该变量在异常处理分支中可能没有被定义。 具体错误出现在更新历史指标的部分: ```python agent.prev_metrics[j_id] = { "avg_delay": alpha * current_avg_delay + (1 - alpha) * agent.prev_metrics[j_id]["avg_delay"], "avg_waiting": alpha * current_avg_waiting + (1 - alpha) * agent.prev_metrics[j_id]["avg_waiting"], "avg_queue": alpha * current_avg_queue + (1 - alpha) * agent.prev_metrics[j_id]["avg_queue"], "travel_reward": alpha * travel_reward + (1 - alpha) * agent.prev_metrics[j_id]["travel_reward"] } ``` 这里使用了`travel_reward`变量,但是如果在计算过程中出现异常,`travel_reward`可能没有被赋值。 在代码中,我们确实在核心奖励计算部分调用了`calculate_travel_reward`函数,并将结果赋值给了`travel_reward`变量,但是这段代码被包含在一个大的try-except块中。如果在计算`travel_reward`之前的代码出现异常(比如获取指标时出错),那么`travel_reward`变量就不会被定义,从而导致在更新历史指标时出现`NameError`。 解决方案: 1. 在try块内部,确保`travel_reward`变量在更新历史指标之前被定义。 2. 在except块中,为`travel_reward`变量提供一个默认值,以确保后续更新历史指标时不会出现未定义错误。 修改步骤: - 在try块中,计算`travel_reward`的部分,如果计算过程中出现异常,我们捕获并设置默认值,同时记录错误。 - 或者,在更新历史指标之前,确保`travel_reward`已经被定义,即使是在异常情况下也提供一个默认值。 我们将在每个路口的try块内部,在计算通行奖励的地方添加异常处理,并确保`travel_reward`变量被定义。 具体修改如下: ```python # 在try块内部,计算通行奖励的部分 try: # 5. 通行奖励:应用天气影响(天气差时降低通行奖励) travel_reward_val = calculate_travel_reward(j_id, fp, vehicles, invalid_lanes) travel_reward_val *= weather_factors["travel"] # 应用天气对通行的影响 rewards[j_id]['components']['travel'] = 0.15 * travel_reward_val except Exception as e: agent.logger.error(f"Error calculating travel reward for junction {j_id}: {str(e)}") travel_reward_val = 0.0 rewards[j_id]['components']['travel'] = 0.0 ``` 然后在更新历史指标时,使用`travel_reward_val`(注意我们重命名了变量以避免冲突): ```python agent.prev_metrics[j_id] = { "avg_delay": alpha * current_avg_delay + (1 - alpha) * agent.prev_metrics[j_id]["avg_delay"], "avg_waiting": alpha * current_avg_waiting + (1 - alpha) * agent.prev_metrics[j_id]["avg_waiting"], "avg_queue": alpha * current_avg_queue + (1 - alpha) * agent.prev_metrics[j_id]["avg_queue"], "travel_reward": alpha * travel_reward_val + (1 - alpha) * agent.prev_metrics[j_id]["travel_reward"] } ``` 这样,无论计算通行奖励是否出现异常,`travel_reward_val`都会被定义(在异常情况下为0.0),从而避免出现`NameError`。 另外,我们注意到在日志中还有另一个问题:动作数量不匹配(2个动作对应4个路口)。我们已经在代码中进行了填充,但需要确保填充逻辑正确。当前代码是: ```python if len(act_list) > len(junction_ids): act_list = act_list[:len(junction_ids)] else: act_list += [0] * (len(junction_ids) - len(act_list)) ``` 这是合理的,将多余的动作截断,不足的动作补0。 但是,根据日志,我们填充后每个路口的动作分别是1,2,0,0(前两个是实际动作,后两个是填充的0)。然后,在相位映射中,我们没有找到相位1和相位2对应的车道(可能是因为这些相位不存在于映射中),所以使用了进口道作为降级处理。这可能会导致奖励计算不准确,但不会导致程序崩溃。 最后,我们需要确保在更新历史指标之前,所有需要的变量(包括`travel_reward_val`)都已经被定义。 下面是修改后的完整代码,重点修复了`travel_reward`未定义的问题,并对其他潜在问题进行了加固: ```python def reward_shaping(_obs, _extra_info, act, agent): # ... [初始化部分保持不变] ... # 逐路口计算奖励(核心逻辑) for j_id in junction_ids: # 为当前路口初始化travel_reward_val,防止未定义 travel_reward_val = 0.0 try: # ... [前面代码保持不变] ... # 5. 通行奖励:应用天气影响(天气差时降低通行奖励) try: # 计算通行奖励 travel_reward_val = calculate_travel_reward(j_id, fp, vehicles, invalid_lanes) travel_reward_val *= weather_factors["travel"] # 应用天气对通行的影响 rewards[j_id]['components']['travel'] = 0.15 * travel_reward_val except Exception as e: agent.logger.error(f"Error calculating travel reward for junction {j_id}: {str(e)}") rewards[j_id]['components']['travel'] = 0.0 # ... [其他奖励计算] ... # 11. 更新历史指标 alpha = 0.3 agent.prev_metrics[j_id] = { "avg_delay": alpha * current_avg_delay + (1 - alpha) * agent.prev_metrics[j_id]["avg_delay"], "avg_waiting": alpha * current_avg_waiting + (1 - alpha) * agent.prev_metrics[j_id]["avg_waiting"], "avg_queue": alpha * current_avg_queue + (1 - alpha) * agent.prev_metrics[j_id]["avg_queue"], "travel_reward": alpha * travel_reward_val + (1 - alpha) * agent.prev_metrics[j_id]["travel_reward"] } except Exception as e: agent.logger.error(f"Error processing junction {j_id}: {str(e)}") rewards[j_id]['total'] = 0.0 # ... [返回奖励] ... ``` 注意:我们在外层try块内部又嵌套了一个try块专门处理通行奖励的计算,这样即使通行奖励计算出错,也不会影响整个路口的处理(只会跳过通行奖励部分),并且会继续更新历史指标(使用0值)。 这样修改后,应该能解决`travel_reward`未定义的问题。 另外,我们注意到错误日志中还有`Action count mismatch`的警告,这表示传入的动作数量与路口数量不一致。我们已经在代码中处理了这种情况,因此这只是警告信息,不会导致程序崩溃。但需要检查为什么动作数量会不一致,这可能是模型输出的问题。 最后,关于相位映射找不到的问题,我们需要确保`fp.get_phase_lanes`或`fp.phase_lane_mapping`能够正确返回车道。如果长期使用进口道作为降级,可能会影响奖励的准确性。因此,建议在系统初始化时验证相位映射的完整性。 现在,我们提供完整的修改后的奖励函数代码(只修改了关键部分,其他部分保持不变):</think>根据错误日志,问题在于 `travel_reward` 变量未定义。这发生在更新历史指标时尝试访问该变量。以下是修复后的完整代码: ```python def reward_shaping(_obs, _extra_info, act, agent): # 1. 输入验证和初始化 if not hasattr(agent, 'preprocess') or not agent.preprocess: raise ValueError("Agent missing required preprocess attribute") fp = agent.preprocess junction_ids = fp.get_sorted_junction_ids() # 动作类型验证和转换 if isinstance(act, (int, np.integer)): act_list = [act] * len(junction_ids) elif isinstance(act, list): act_list = act else: agent.logger.error(f"Invalid action type: {type(act)}. Using default actions.") act_list = [0] * len(junction_ids) # 确保动作数量匹配路口数量 if len(act_list) != len(junction_ids): agent.logger.warning( f"Action count mismatch: {len(act_list)} actions for {len(junction_ids)} junctions. " "Truncating or padding actions." ) if len(act_list) > len(junction_ids): act_list = act_list[:len(junction_ids)] else: act_list += [0] * (len(junction_ids) - len(act_list)) j_id_to_act_idx = {j_id: idx for idx, j_id in enumerate(junction_ids)} rewards = {j_id: {'total': 0.0, 'components': {}} for j_id in junction_ids} # 2. 基础数据获取和验证 if "framestate" not in _obs: agent.logger.error("Missing framestate in observation") return tuple(0.0 for _ in junction_ids) frame_state = _obs["framestate"] vehicles = frame_state.get("vehicles", []) phases = frame_state.get("phases", []) try: fp.update_traffic_info(_obs, _extra_info) except Exception as e: agent.logger.error(f"Failed to update traffic info: {str(e)}") # 3. 核心指标计算(带异常处理) try: all_junction_waiting = fp.get_all_junction_waiting_time(vehicles) invalid_lanes = fp.get_invalid_lanes() global_avg_queue = fp.get_all_avg_queue() except Exception as e: agent.logger.error(f"Error calculating core metrics: {str(e)}") all_junction_waiting = {} invalid_lanes = set() global_avg_queue = 0.0 is_global_congested = global_avg_queue > 5.0 # 4. 天气处理(带默认值) weather_map = {0: "晴", 1: "雨", 2: "雪", 3: "雾"} try: weather = fp.get_weather() weather_name = weather_map.get(weather, "未知") except Exception as e: agent.logger.error(f"Error getting weather: {str(e)}") weather = 0 weather_name = "未知" # 天气影响系数(带默认值) weather_impact = { 0: {"delay": 1.0, "waiting": 1.0, "queue": 1.0, "travel": 1.0, "coord": 1.0}, 1: {"delay": 1.15, "waiting": 1.1, "queue": 1.15, "travel": 0.85, "coord": 1.15}, 2: {"delay": 1.35, "waiting": 1.25, "queue": 1.35, "travel": 0.75, "coord": 1.35}, 3: {"delay": 1.5, "waiting": 1.4, "queue": 1.5, "travel": 0.65, "coord": 1.5} } weather_factors = weather_impact.get(weather, weather_impact[0]) # 5. 历史指标初始化(带类型检查) if not hasattr(agent, 'prev_metrics'): agent.prev_metrics = {} for j_id in junction_ids: try: capacity = fp.get_junction_capacity(j_id) except: capacity = 10 # 默认容量 agent.prev_metrics[j_id] = { "avg_delay": 5.0 + capacity * 0.01, "avg_waiting": 3.0 + capacity * 0.005, "avg_queue": 2.0 + capacity * 0.002, "travel_reward": 0.0 # 初始化 travel_reward } # 6. 逐路口计算奖励(核心逻辑) for j_id in junction_ids: # 初始化 travel_reward_val 为默认值 travel_reward_val = 0.0 try: junction = fp.junction_dict.get(j_id) if not junction: agent.logger.warning(f"Junction {j_id} not found in junction_dict") continue signal_id = junction.get("signal", -1) capacity = fp.get_junction_capacity(j_id) region_id = fp.get_region(j_id) # 7. 动作类型转换和安全访问 j_idx = j_id_to_act_idx.get(j_id, -1) current_act = act_list[j_idx] if 0 <= j_idx < len(act_list) else 0 # 确保动作是整数 if isinstance(current_act, list): if current_act: current_act = current_act[0] agent.logger.warning(f"Converted list action to integer: {current_act}") else: current_act = 0 elif not isinstance(current_act, (int, np.integer)): agent.logger.warning(f"Invalid action type {type(current_act)} for junction {j_id}") current_act = 0 # 8. 相位车道获取(带安全检查和降级处理) phase_lanes = [] try: if hasattr(fp, 'get_phase_lanes'): phase_lanes = fp.get_phase_lanes(signal_id, current_act) elif hasattr(fp, 'phase_lane_mapping'): phase_map = fp.phase_lane_mapping.get(signal_id, {}) phase_lanes = phase_map.get(current_act, []) except Exception as e: agent.logger.error(f"Error getting phase lanes: {str(e)}") if not phase_lanes: agent.logger.warning(f"Junction {j_id}: No lanes for phase {current_act}, using enter lanes") try: phase_lanes = junction.get("cached_enter_lanes", []) except: phase_lanes = [] # 9. 指标计算(带异常处理) try: current_avg_delay = fp.get_junction_avg_delay(j_id) current_avg_queue = fp.get_junction_avg_queue(j_id) current_avg_waiting = all_junction_waiting.get(j_id, 0.0) except Exception as e: agent.logger.error(f"Error getting metrics for junction {j_id}: {str(e)}") current_avg_delay = agent.prev_metrics[j_id]["avg_delay"] current_avg_queue = agent.prev_metrics[j_id]["avg_queue"] current_avg_waiting = agent.prev_metrics[j_id]["avg_waiting"] # 10. 奖励计算(核心不变) # ... [保留原有的奖励计算逻辑] ... # 确保在奖励计算中正确设置 travel_reward_val try: travel_reward_val = calculate_travel_reward(j_id, fp, vehicles, invalid_lanes) travel_reward_val *= weather_factors["travel"] rewards[j_id]['components']['travel'] = 0.15 * travel_reward_val except Exception as e: agent.logger.error(f"Error calculating travel reward for junction {j_id}: {str(e)}") travel_reward_val = 0.0 # 11. 更新历史指标 alpha = 0.3 agent.prev_metrics[j_id] = { "avg_delay": alpha * current_avg_delay + (1 - alpha) * agent.prev_metrics[j_id]["avg_delay"], "avg_waiting": alpha * current_avg_waiting + (1 - alpha) * agent.prev_metrics[j_id]["avg_waiting"], "avg_queue": alpha * current_avg_queue + (1 - alpha) * agent.prev_metrics[j_id]["avg_queue"], # 使用 travel_reward_val 而不是未定义的 travel_reward "travel_reward": alpha * travel_reward_val + (1 - alpha) * agent.prev_metrics[j_id]["travel_reward"] } except Exception as e: agent.logger.error(f"Error processing junction {j_id}: {str(e)}") rewards[j_id]['total'] = 0.0 # 12. 返回各路口总奖励 return tuple(rewards[j_id].get('total', 0.0) for j_id in junction_ids) def calculate_travel_reward(junction_id, fp, vehicles, invalid_lanes, distance_scale=100.0): try: total_distance = 0.0 valid_count = 0 completed_count = 0 base_entry_reward = 5.0 rewarded_completed = set() # 获取车辆信息 vehicle_dict = {v["v_id"]: v for v in vehicles} if vehicles else {} vehicle_type_weight = {1: 1.0, 2: 1.5, 3: 1.2, 4: 0.8, 5: 0.5} current_v_ids = {v["v_id"] for v in vehicles} if vehicles else set() # 获取完成的车辆信息 completed_vehicles = getattr(fp.junction_metrics.get(junction_id, {}), "completed_vehicles", set()) # 遍历车辆行驶距离,计算奖励 for v_id, distance in getattr(fp, 'vehicle_distance_store', {}).items(): if v_id not in current_v_ids or v_id not in vehicle_dict: continue vehicle = vehicle_dict[v_id] # 行驶中车辆奖励 if (vehicle.get("target_junction", -1) == junction_id and (on_enter_lane(vehicle, invalid_lanes) or in_junction(vehicle)) and v_id not in completed_vehicles): if vehicle.get("lane", -1) in invalid_lanes or fp.vehicle_status.get(v_id, 0) != 0: continue # 车型加权 v_config_id = vehicle.get("v_config_id", 1) v_type = getattr(fp.vehicle_configs.get(v_config_id, {}), "v_type", 1) weight = vehicle_type_weight.get(v_type, 1.0) total_distance += (distance / distance_scale) * weight valid_count += 1 # 已通过车辆奖励 if v_id in completed_vehicles and v_id not in rewarded_completed and completed_count < 5: total_distance += base_entry_reward * 2 completed_count += 1 rewarded_completed.add(v_id) # 计算最终通行奖励 total_count = valid_count + completed_count if total_count > 0: avg_distance = total_distance / total_count count_bonus = min(0.3, total_count * 0.01) return np.tanh(avg_distance + count_bonus) else: return 0.1 except Exception as e: if hasattr(fp, 'logger'): fp.logger.error(f"Error in calculate_travel_reward: {str(e)}") return 0.0 ``` ### 关键修复说明 #### 1. 修复 `travel_reward` 未定义问题 ```python # 在 try 块开始时初始化 travel_reward_val travel_reward_val = 0.0 # 默认值 # 在奖励计算部分使用 travel_reward_val travel_reward_val = calculate_travel_reward(j_id, fp, vehicles, invalid_lanes) travel_reward_val *= weather_factors["travel"] rewards[j_id]['components']['travel'] = 0.15 * travel_reward_val # 在更新历史指标时使用 travel_reward_val "travel_reward": alpha * travel_reward_val + (1 - alpha) * agent.prev_metrics[j_id]["travel_reward"] ``` #### 2. 增强 `calculate_travel_reward` 的健壮性 ```python def calculate_travel_reward(junction_id, fp, vehicles, invalid_lanes, distance_scale=100.0): try: # 安全访问属性 vehicle_dict = {v["v_id"]: v for v in vehicles} if vehicles else {} completed_vehicles = getattr(fp.junction_metrics.get(junction_id, {}), "completed_vehicles", set()) # 安全访问车辆配置 v_config_id = vehicle.get("v_config_id", 1) v_type = getattr(fp.vehicle_configs.get(v_config_id, {}), "v_type", 1) # 安全访问距离存储 for v_id, distance in getattr(fp, 'vehicle_distance_store', {}).items(): # ... except Exception as e: # 错误处理 return 0.0 ``` #### 3. 添加防御性属性访问 ```python # 安全访问 junction_metrics completed_vehicles = getattr(fp.junction_metrics.get(junction_id, {}), "completed_vehicles", set()) # 安全访问 vehicle_distance_store for v_id, distance in getattr(fp, 'vehicle_distance_store', {}).items(): # ... ``` #### 4. 初始化 `travel_reward` 历史值 ```python # 在历史指标初始化中添加 travel_reward agent.prev_metrics[j_id] = { # ... 其他指标 ... "travel_reward": 0.0 # 初始化 travel_reward } ``` ### 其他优化点 1. **安全属性访问**: ```python # 使用 getattr 和默认值 completed_vehicles = getattr(fp.junction_metrics.get(junction_id, {}), "completed_vehicles", set()) ``` 2. **空值处理**: ```python # 处理空车辆列表 vehicle_dict = {v["v_id"]: v for v in vehicles} if vehicles else {} current_v_ids = {v["v_id"] for v in vehicles} if vehicles else set() ``` 3. **默认值设置**: ```python # 为关键属性提供默认值 v_config_id = vehicle.get("v_config_id", 1) v_type = getattr(fp.vehicle_configs.get(v_config_id, {}), "v_type", 1) ``` 4. **错误隔离**: ```python # 将 travel_reward 计算隔离在单独的 try-except 中 try: travel_reward_val = calculate_travel_reward(...) except Exception as e: # 错误处理 ``` ### 错误原因分析 1. **变量作用域问题**: - 原始代码中 `travel_reward` 是在奖励计算部分定义的 - 但在异常情况下,该变量可能未被定义 - 在更新历史指标时访问了未定义的变量 2. **初始化缺失**: - 历史指标中的 `travel_reward` 未正确初始化 - 导致在第一次访问时出现问题 3. **属性访问不安全**: - `fp.junction_metrics` 等属性访问没有防御性检查 - 在属性缺失时导致异常 ### 预期效果 1. **解决 `travel_reward` 未定义错误**: - 确保变量在作用域内始终定义 - 提供默认值防止未定义访问 2. **增强系统稳定性**: - 防御性属性访问防止属性缺失导致的崩溃 - 空值处理确保函数在各种情况下都能运行 3. **提高错误恢复能力**: - 隔离关键计算部分的错误 - 提供有意义的错误日志帮助诊断问题 4. **保持功能一致性**: - 核心奖励计算逻辑保持不变 - 仅在异常情况下使用降级方案 这个修改版本解决了日志中报告的所有错误,同时保持了原有功能,并增强了系统的健壮性。
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