优化车辆生成位置偏差问题，新增红绿灯信息采集方法

Env/run_multiagent_env_parallel.py

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+"""
+多进程并行版本 - 充分利用多核CPU
+适合大规模数据收集和训练
+"""
+from scenario_env import MultiAgentScenarioEnv
+from simple_idm_policy import ConstantVelocityPolicy
+from metadrive.engine.asset_loader import AssetLoader
+import time
+import os
+from multiprocessing import Pool, cpu_count
+
+WAYMO_DATA_DIR = r"/home/huangfukk/MAGAIL4AutoDrive/Env"
+
+
+def run_single_env(args):
+    """在单个进程中运行一个环境实例"""
+    seed, num_steps, worker_id = args
+    
+    # 创建环境（每个进程独立）
+    env = MultiAgentScenarioEnv(
+        config={
+            "data_directory": AssetLoader.file_path(WAYMO_DATA_DIR, "exp_converted", unix_style=False),
+            "is_multi_agent": True,
+            "num_controlled_agents": 3,
+            "horizon": 300,
+            
+            # 性能优化
+            "use_render": False,
+            "render_pipeline": False,
+            "image_observation": False,
+            "interface_panel": [],
+            "manual_control": False,
+            "show_fps": False,
+            "debug": False,
+            
+            "physics_world_step_size": 0.02,
+            "decision_repeat": 5,
+            "sequential_seed": True,
+            "reactive_traffic": True,
+            
+            # 车道检测与过滤配置
+            "filter_offroad_vehicles": True,
+            "lane_tolerance": 3.0,
+            "max_controlled_vehicles": 15,
+        },
+        agent2policy=ConstantVelocityPolicy(target_speed=50)
+    )
+    
+    # 启用激光雷达缓存
+    env.lidar_cache_interval = 3
+    
+    # 运行仿真
+    start_time = time.time()
+    obs = env.reset(seed)
+    total_steps = 0
+    total_agents = 0
+    
+    for step in range(num_steps):
+        actions = {
+            aid: env.controlled_agents[aid].policy.act()
+            for aid in env.controlled_agents
+        }
+        
+        obs, rewards, dones, infos = env.step(actions)
+        total_steps += 1
+        total_agents += len(env.controlled_agents)
+        
+        if dones["__all__"]:
+            break
+    
+    elapsed = time.time() - start_time
+    fps = total_steps / elapsed if elapsed > 0 else 0
+    avg_agents = total_agents / total_steps if total_steps > 0 else 0
+    
+    env.close()
+    
+    return {
+        'worker_id': worker_id,
+        'seed': seed,
+        'steps': total_steps,
+        'elapsed': elapsed,
+        'fps': fps,
+        'avg_agents': avg_agents,
+    }
+
+
+def main():
+    """主函数：协调多个并行环境"""
+    # 获取CPU核心数
+    num_cores = cpu_count()
+    # 建议使用物理核心数（12600KF是10核20线程，使用10个进程）
+    num_workers = min(10, num_cores)
+    
+    print("=" * 80)
+    print(f"多进程并行模式")
+    print(f"CPU核心数: {num_cores}")
+    print(f"并行进程数: {num_workers}")
+    print(f"每个环境运行: 1000步")
+    print("=" * 80)
+    
+    # 准备任务参数
+    num_steps_per_env = 1000
+    tasks = [(seed, num_steps_per_env, worker_id) 
+             for worker_id, seed in enumerate(range(num_workers))]
+    
+    # 启动多进程池
+    start_time = time.time()
+    
+    with Pool(processes=num_workers) as pool:
+        results = pool.map(run_single_env, tasks)
+    
+    total_elapsed = time.time() - start_time
+    
+    # 统计结果
+    print("\n" + "=" * 80)
+    print("各进程执行结果：")
+    print("-" * 80)
+    print(f"{'Worker':<8} {'Seed':<6} {'Steps':<8} {'Time(s)':<10} {'FPS':<8} {'平均车辆数':<12}")
+    print("-" * 80)
+    
+    total_steps = 0
+    total_fps = 0
+    
+    for result in results:
+        print(f"{result['worker_id']:<8} "
+              f"{result['seed']:<6} "
+              f"{result['steps']:<8} "
+              f"{result['elapsed']:<10.2f} "
+              f"{result['fps']:<8.2f} "
+              f"{result['avg_agents']:<12.1f}")
+        total_steps += result['steps']
+        total_fps += result['fps']
+    
+    print("-" * 80)
+    avg_fps_per_env = total_fps / len(results)
+    total_throughput = total_steps / total_elapsed
+    
+    print(f"\n总体统计：")
+    print(f"  总步数: {total_steps}")
+    print(f"  总耗时: {total_elapsed:.2f}s")
+    print(f"  单环境平均FPS: {avg_fps_per_env:.2f}")
+    print(f"  总吞吐量: {total_throughput:.2f} steps/s")
+    print(f"  并行效率: {total_throughput / avg_fps_per_env:.1f}x")
+    print("=" * 80)
+    
+    # 与单进程对比
+    print(f"\n性能对比：")
+    print(f"  单进程FPS (预估): ~30 FPS")
+    print(f"  多进程吞吐量: {total_throughput:.2f} steps/s")
+    print(f"  性能提升: {total_throughput / 30:.1f}x")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()
+