MAGAIL4AutoDrive/Env/run_multiagent_env.py

import argparse
from scenario_env import MultiAgentScenarioEnv
from simple_idm_policy import ConstantVelocityPolicy
from replay_policy import ReplayPolicy
from metadrive.engine.asset_loader import AssetLoader

WAYMO_DATA_DIR = r"/home/huangfukk/mdsn/exp_converted"


def run_replay_mode(data_dir, num_episodes=1, horizon=300, render=True, debug=False,
                    scenario_id=None, use_scenario_duration=False,
                    spawn_vehicles=True, spawn_pedestrians=True, spawn_cyclists=True):
    """
    回放模式：严格按照专家轨迹回放

    Args:
        data_dir: 数据目录
        num_episodes: 回合数（如果指定scenario_id，则忽略）
        horizon: 最大步数（如果use_scenario_duration=True，则自动设置）
        render: 是否渲染
        debug: 是否调试模式
        scenario_id: 指定场景ID（可选）
        use_scenario_duration: 是否使用场景原始时长
        spawn_vehicles: 是否生成车辆（默认True）
        spawn_pedestrians: 是否生成行人（默认True）
        spawn_cyclists: 是否生成自行车（默认True）
    """
    print("=" * 50)
    print("运行模式: 专家轨迹回放 (Replay Mode)")
    if scenario_id is not None:
        print(f"指定场景ID: {scenario_id}")
    if use_scenario_duration:
        print("使用场景原始时长")
    print("=" * 50)

    # 如果指定了场景ID，只运行1个回合
    if scenario_id is not None:
        num_episodes = 1

    # ✅ 环境创建移到循环外面，避免重复创建
    env = MultiAgentScenarioEnv(
        config={
            "data_directory": AssetLoader.file_path(data_dir, "training_20s", unix_style=False),
            "is_multi_agent": True,
            "horizon": horizon,
            "use_render": render,
            "sequential_seed": True,
            "reactive_traffic": False,  # 回放模式下不需要反应式交通
            "manual_control": False,
            "filter_offroad_vehicles": True,  # 启用车道过滤
            "lane_tolerance": 3.0,
            "replay_mode": True,  # 标记为回放模式
            "debug": debug,
            "specific_scenario_id": scenario_id,  # 指定场景ID
            "use_scenario_duration": use_scenario_duration,  # 使用场景时长
            # 对象类型过滤
            "spawn_vehicles": spawn_vehicles,
            "spawn_pedestrians": spawn_pedestrians,
            "spawn_cyclists": spawn_cyclists,
        },
        agent2policy=None  # 回放模式不需要统一策略
    )

    try:
        for episode in range(num_episodes):
            print(f"\n{'='*50}")
            print(f"回合 {episode + 1}/{num_episodes}")
            if scenario_id is not None:
                print(f"场景ID: {scenario_id}")
            print(f"{'='*50}")

            # ✅ 如果不是指定场景，使用seed来遍历不同场景
            seed = scenario_id if scenario_id is not None else episode
            obs = env.reset(seed=seed)

            # 为每个车辆分配 ReplayPolicy
            replay_policies = {}
            for agent_id, vehicle in env.controlled_agents.items():
                vehicle_id = vehicle.expert_vehicle_id
                if vehicle_id in env.expert_trajectories:
                    replay_policy = ReplayPolicy(
                        env.expert_trajectories[vehicle_id],
                        vehicle_id
                    )
                    vehicle.set_policy(replay_policy)
                    replay_policies[agent_id] = replay_policy

            # 输出场景信息
            actual_horizon = env.config["horizon"]
            print(f"初始化完成:")
            print(f"  可控车辆数: {len(env.controlled_agents)}")
            print(f"  专家轨迹数: {len(env.expert_trajectories)}")
            print(f"  场景时长: {env.scenario_max_duration} 步")
            print(f"  实际Horizon: {actual_horizon} 步")

            step_count = 0
            active_vehicles_count = []

            while True:
                # 在回放模式下，直接使用专家轨迹设置车辆状态
                for agent_id, vehicle in list(env.controlled_agents.items()):
                    vehicle_id = vehicle.expert_vehicle_id
                    if vehicle_id in env.expert_trajectories and agent_id in replay_policies:
                        target_state = replay_policies[agent_id].get_target_state(env.round)
                        if target_state is not None:
                            # 直接设置车辆状态（绕过物理引擎）
                            # 只使用xy坐标，保持车辆在地面上
                            position_2d = target_state['position'][:2]
                            vehicle.set_position(position_2d)
                            vehicle.set_heading_theta(target_state['heading'])
                            vehicle.set_velocity(target_state['velocity'][:2] if len(target_state['velocity']) > 2 else target_state['velocity'])

                # 使用空动作进行步进
                actions = {aid: [0.0, 0.0] for aid in env.controlled_agents}
                obs, rewards, dones, infos = env.step(actions)

                if render:
                    env.render(mode="topdown")

                step_count += 1
                active_vehicles_count.append(len(env.controlled_agents))

                # 每50步打印一次状态
                if step_count % 50 == 0:
                    print(f"Step {step_count}: {len(env.controlled_agents)} 辆活跃车辆")
                    
                    # 调试模式下打印车辆高度信息
                    if debug and len(env.controlled_agents) > 0:
                        sample_vehicle = list(env.controlled_agents.values())[0]
                        z_pos = sample_vehicle.position[2] if len(sample_vehicle.position) > 2 else 0
                        print(f"  [DEBUG] 示例车辆高度: z={z_pos:.3f}m")

                if dones["__all__"]:
                    print(f"\n回合结束统计:")
                    print(f"  总步数: {step_count}")
                    print(f"  最大同时车辆数: {max(active_vehicles_count) if active_vehicles_count else 0}")
                    print(f"  平均车辆数: {sum(active_vehicles_count) / len(active_vehicles_count) if active_vehicles_count else 0:.1f}")
                    if use_scenario_duration:
                        print(f"  场景完整回放: {'是' if step_count >= env.scenario_max_duration else '否'}")
                    break
    finally:
        # ✅ 确保环境被正确关闭
        env.close()

    print("\n" + "=" * 50)
    print("回放完成！")
    print("=" * 50)


def run_simulation_mode(data_dir, num_episodes=1, horizon=300, render=True, debug=False,
                        scenario_id=None, use_scenario_duration=False,
                        spawn_vehicles=True, spawn_pedestrians=True, spawn_cyclists=True):
    """
    仿真模式：使用自定义策略控制车辆
    车辆根据专家数据的初始位姿生成，然后由策略控制

    Args:
        data_dir: 数据目录
        num_episodes: 回合数
        horizon: 最大步数
        render: 是否渲染
        debug: 是否调试模式
        scenario_id: 指定场景ID（可选）
        use_scenario_duration: 是否使用场景原始时长
        spawn_vehicles: 是否生成车辆（默认True）
        spawn_pedestrians: 是否生成行人（默认True）
        spawn_cyclists: 是否生成自行车（默认True）
    """
    print("=" * 50)
    print("运行模式: 策略仿真 (Simulation Mode)")
    if scenario_id is not None:
        print(f"指定场景ID: {scenario_id}")
    if use_scenario_duration:
        print("使用场景原始时长")
    print("=" * 50)

    # 如果指定了场景ID，只运行1个回合
    if scenario_id is not None:
        num_episodes = 1

    env = MultiAgentScenarioEnv(
        config={
            "data_directory": AssetLoader.file_path(data_dir, "training_20s", unix_style=False),
            "is_multi_agent": True,
            "num_controlled_agents": 3,
            "horizon": horizon,
            "use_render": render,
            "sequential_seed": True,
            "reactive_traffic": True,
            "manual_control": False,
            "filter_offroad_vehicles": True,  # 启用车道过滤
            "lane_tolerance": 3.0,
            "replay_mode": False,  # 仿真模式
            "debug": debug,
            "specific_scenario_id": scenario_id,  # 指定场景ID
            "use_scenario_duration": use_scenario_duration,  # 使用场景时长
            # 对象类型过滤
            "spawn_vehicles": spawn_vehicles,
            "spawn_pedestrians": spawn_pedestrians,
            "spawn_cyclists": spawn_cyclists,
        },
        agent2policy=ConstantVelocityPolicy(target_speed=50)
    )

    try:
        for episode in range(num_episodes):
            print(f"\n{'='*50}")
            print(f"回合 {episode + 1}/{num_episodes}")
            if scenario_id is not None:
                print(f"场景ID: {scenario_id}")
            print(f"{'='*50}")

            seed = scenario_id if scenario_id is not None else episode
            obs = env.reset(seed=seed)

            actual_horizon = env.config["horizon"]
            print(f"初始化完成:")
            print(f"  可控车辆数: {len(env.controlled_agents)}")
            print(f"  场景时长: {env.scenario_max_duration} 步")
            print(f"  实际Horizon: {actual_horizon} 步")

            step_count = 0
            total_reward = 0.0

            while True:
                # 使用策略生成动作
                actions = {
                    aid: env.controlled_agents[aid].policy.act()
                    for aid in env.controlled_agents
                }

                obs, rewards, dones, infos = env.step(actions)

                if render:
                    env.render(mode="topdown")

                step_count += 1
                total_reward += sum(rewards.values())

                # 每50步打印一次状态
                if step_count % 50 == 0:
                    print(f"Step {step_count}: {len(env.controlled_agents)} 辆活跃车辆")

                if dones["__all__"]:
                    print(f"\n回合结束统计:")
                    print(f"  总步数: {step_count}")
                    print(f"  总奖励: {total_reward:.2f}")
                    break
    finally:
        env.close()

    print("\n" + "=" * 50)
    print("仿真完成！")
    print("=" * 50)


def main():
    parser = argparse.ArgumentParser(description="MetaDrive 多智能体环境运行脚本")

    parser.add_argument(
        "--mode",
        type=str,
        choices=["replay", "simulation"],
        default="simulation",
        help="运行模式: replay=专家轨迹回放, simulation=策略仿真 (默认: simulation)"
    )

    parser.add_argument(
        "--data_dir",
        type=str,
        default=WAYMO_DATA_DIR,
        help=f"数据目录路径 (默认: {WAYMO_DATA_DIR})"
    )

    parser.add_argument(
        "--episodes",
        type=int,
        default=1,
        help="运行回合数 (默认: 1)"
    )

    parser.add_argument(
        "--horizon",
        type=int,
        default=300,
        help="每回合最大步数 (默认: 300，如果启用 --use_scenario_duration 则自动设置)"
    )

    parser.add_argument(
        "--no_render",
        action="store_true",
        help="禁用渲染（加速运行）"
    )

    parser.add_argument(
        "--debug",
        action="store_true",
        help="启用调试模式（显示详细日志）"
    )

    parser.add_argument(
        "--scenario_id",
        type=int,
        default=None,
        help="指定场景ID（可选，如指定则只运行该场景）"
    )

    parser.add_argument(
        "--use_scenario_duration",
        action="store_true",
        help="使用场景原始时长作为horizon（自动停止）"
    )

    parser.add_argument(
        "--no_vehicles",
        action="store_true",
        help="禁止生成车辆"
    )

    parser.add_argument(
        "--no_pedestrians",
        action="store_true",
        help="禁止生成行人"
    )

    parser.add_argument(
        "--no_cyclists",
        action="store_true",
        help="禁止生成自行车"
    )

    args = parser.parse_args()

    if args.mode == "replay":
        run_replay_mode(
            data_dir=args.data_dir,
            num_episodes=args.episodes,
            horizon=args.horizon,
            render=not args.no_render,
            debug=args.debug,
            scenario_id=args.scenario_id,
            use_scenario_duration=args.use_scenario_duration,
            spawn_vehicles=not args.no_vehicles,
            spawn_pedestrians=not args.no_pedestrians,
            spawn_cyclists=not args.no_cyclists
        )
    else:
        run_simulation_mode(
            data_dir=args.data_dir,
            num_episodes=args.episodes,
            horizon=args.horizon,
            render=not args.no_render,
            debug=args.debug,
            scenario_id=args.scenario_id,
            use_scenario_duration=args.use_scenario_duration,
            spawn_vehicles=not args.no_vehicles,
            spawn_pedestrians=not args.no_pedestrians,
            spawn_cyclists=not args.no_cyclists
        )


if __name__ == "__main__":
    main()