完善项目目录结构

.gitignore

@@ -1,3 +1,57 @@
 # 日志文件
 Env/logs/
 *.log
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# 虚拟环境
+venv/
+env/
+ENV/
+.venv
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# 数据和模型文件
+data/
+runs/
+*.pkl
+*.h5
+*.ckpt
+*.pth
+*.pt
+checkpoints/
+models/
+
+# 第三方库（如果已安装）
+metadrive/
+scenarionet/
+
+# 系统文件
+.DS_Store
+Thumbs.db

Algorithm/bc.py

@@ -0,0 +1,52 @@
+"""
+Behavior Cloning (BC) 算法：仅包含损失与单 epoch 训练/评估逻辑。
+数据加载、环境评估、日志与保存由训练脚本 (train_bc.py) 负责。
+"""
+import torch
+
+
+def bc_loss(policy, states, actions):
+    """
+    BC 损失：负对数似然 -E[log pi(a|s)]。
+    states: (B, state_dim), actions: (B, action_dim), 均在 policy 所在 device 上。
+    """
+    log_pi = policy.evaluate_log_pi(states, actions)
+    return -log_pi.mean()
+
+
+def train_bc_epoch(policy, train_loader, optimizer, device):
+    """
+    训练一个 epoch，返回平均 train loss。
+    policy 与 optimizer 由调用方管理，本函数只做前向、损失、反向与 step。
+    """
+    policy.train()
+    total_loss = 0.0
+    n_batches = 0
+    for states, actions in train_loader:
+        states = states.to(device)
+        actions = actions.to(device)
+        loss = bc_loss(policy, states, actions)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        total_loss += loss.item()
+        n_batches += 1
+    return total_loss / n_batches if n_batches else 0.0
+
+
+def eval_bc_epoch(policy, val_loader, device):
+    """
+    在验证集上评估一个 epoch，返回平均 val loss（无梯度）。
+    """
+    policy.eval()
+    total_loss = 0.0
+    n_batches = 0
+    with torch.no_grad():
+        for states, actions in val_loader:
+            states = states.to(device)
+            actions = actions.to(device)
+            log_pi = policy.evaluate_log_pi(states, actions)
+            loss = -log_pi.mean().item()
+            total_loss += loss
+            n_batches += 1
+    return total_loss / n_batches if n_batches else 0.0

Env/bc_env.py

@@ -0,0 +1,64 @@
+from Env.scenario_env import MultiAgentScenarioEnv
+import numpy as np
+
+class BCScenarioEnv(MultiAgentScenarioEnv):
+    """
+    Environment for Behavior Cloning Evaluation.
+    Uses the same 45-dim observation as ExpertReplayEnv:
+    - Ego State (5): x, y, vx, vy, heading
+    - Neighbors (40): 10 nearest * (rel_x, rel_y, vx, vy)
+    """
+    def _get_all_obs(self):
+        # Implement custom observation: 30m range, 10 nearest vehicles
+        obs_dict = {}
+        
+        for agent_id, vehicle in self.controlled_agents.items():
+            # 1. Ego State
+            ego_state = [
+                vehicle.position[0], vehicle.position[1],
+                vehicle.velocity[0], vehicle.velocity[1],
+                vehicle.heading_theta
+            ]
+            
+            # 2. Neighbors
+            neighbors = []
+            # Iterate through all vehicles in the engine
+            candidates = []
+            # Use engine.agent_manager.active_agents to find neighbors
+            # Note: This includes background vehicles if they are in active_agents
+            for other_id, other_vehicle in self.engine.agent_manager.active_agents.items():
+                if other_id == agent_id:
+                    continue
+                
+                # Check if vehicle is valid/active
+                # (MetaDrive manages active_agents, so they should be active)
+                
+                dist = np.linalg.norm(vehicle.position - other_vehicle.position)
+                if dist < 30.0:
+                    candidates.append((dist, other_vehicle))
+            
+            # Sort by distance
+            candidates.sort(key=lambda x: x[0])
+            
+            # Take top 10
+            top_10 = candidates[:10]
+            
+            neighbor_feats = []
+            for _, neighbor in top_10:
+                neighbor_feats.extend([
+                    neighbor.position[0] - vehicle.position[0], # Relative pos
+                    neighbor.position[1] - vehicle.position[1],
+                    neighbor.velocity[0], # Absolute vel
+                    neighbor.velocity[1]
+                ])
+                
+            # Pad if < 10
+            missing = 10 - len(top_10)
+            if missing > 0:
+                neighbor_feats.extend([0.0] * (4 * missing))
+                
+            # Flatten
+            obs = np.array(ego_state + neighbor_feats, dtype=np.float32)
+            obs_dict[agent_id] = obs
+            
+        return obs_dict

Env/scenario_env.py

@@ -100,6 +100,13 @@ class MultiAgentScenarioEnv(ScenarioEnv):
         for scenario_id in _obj_to_clean_this_frame:
             self.engine.traffic_manager.current_traffic_data.pop(scenario_id)
 
+        # Clear vehicles we spawned via engine.spawn_object() so _object_clean_check() passes
+        ids_to_clear = [v.id for v in self.controlled_agents.values()]
+        if ids_to_clear:
+            self.engine.clear_objects(ids_to_clear)
+        self.controlled_agents.clear()
+        self.controlled_agent_ids.clear()
+
         self.engine.reset()
         self.reset_sensors()
         self.engine.taskMgr.step()
@@ -114,9 +121,6 @@ class MultiAgentScenarioEnv(ScenarioEnv):
         self.episode_rewards = defaultdict(float)
         self.episode_lengths = defaultdict(int)
 
-        self.controlled_agents.clear()
-        self.controlled_agent_ids.clear()
-
         super().reset(seed)  # 初始化场景
         self._spawn_controlled_agents()
 
@@ -190,6 +194,7 @@ class MultiAgentScenarioEnv(ScenarioEnv):
                 self.controlled_agents[agent_id].before_step(action)
 
         self.engine.step()
+        self.engine.after_step()
 
         for agent_id in action_dict:
             if agent_id in self.controlled_agents:

README.md

@@ -1,98 +1,121 @@
 # MAGAIL4AutoDrive
 
-> 基于多智能体生成对抗模仿学习(MAGAIL)的自动驾驶训练系统 | MetaDrive + Waymo Open Motion Dataset
+基于 **MetaDrive** 仿真器和 **Waymo Open Motion Dataset** 的自动驾驶多智能体模仿学习（MAGAIL）与行为克隆（BC）训练系统。
 
-本项目利用 Waymo 真实驾驶数据，通过 MetaDrive 仿真环境构建专家回放系统，提取车辆状态与动作，用于训练多智能体模仿学习算法 (MAGAIL)。
+本项目旨在从真实的 Waymo 驾驶数据中提取专家轨迹，并通过模仿学习（Imitation Learning）训练能够适应复杂交互场景的自动驾驶策略。
 
-## 📁 核心模块
+## 目录结构
 
-*   **`Env/expert_replay_env.py`**: 专家回放环境。核心类 `ExpertReplayEnv`，负责读取 Waymo 轨迹，计算逆动力学动作，并过滤非道路/静态车辆。
+```text
-*   **`Env/inverse_dynamics.py`**: 逆动力学模块。根据车辆位置和航向计算油门、刹车和转向动作。
+MAGAIL4AutoDrive/
-*   **`scripts/generate_expert_data.py`**: 数据收集脚本。批量运行场景并保存训练数据。
+├── Algorithm/                 # 强化学习与模仿学习算法实现
-*   **`scripts/visualize_replay.py`**: 可视化脚本。用于观察回放效果和数据质量。
+│   ├── policy.py              # 基础策略网络 (MLP 等)
-
+│   ├── ppo.py                 # PPO 算法实现
-***
+│   ├── magail.py              # MAGAIL 算法核心逻辑
-
+│   ├── disc.py                # 判别器 (Discriminator) 网络
-## 🚀 1. 数据收集
+│   └── ...
-
+├── Env/                       # 仿真环境封装 (MetaDrive Wrapper)
-### 生成专家数据
+│   ├── bc_env.py              # BCScenarioEnv，45 维观测（BC/MAGAIL 共用）
-使用 `generate_expert_data.py` 脚本从 Waymo 数据集中批量提取 (State, Action) 对。
+│   ├── scenario_env.py       # 多智能体基础场景环境
-
+│   ├── expert_replay_env.py  # 专家轨迹回放环境（数据生成与回放）
-```bash
+│   ├── inverse_dynamics.py   # 逆动力学模块 (轨迹 -> 动作)
-# 设置 Python 路径
+│   ├── simple_idm_policy.py  # ConstantVelocityPolicy 占位策略
-export PYTHONPATH=$PYTHONPATH:.:./metadrive
+│   └── ...
-
+├── dataset/                   # 数据集加载器
-# 运行生成脚本
+│   ├── loader.py              # 主流水线：load_expert_pkl、MAGAILExpertDataset
-# --data_dir: Waymo 数据路径 (建议使用 exp_filtered)
+│   └── expert_dataset.py      # 可选 107 维/5 维管线
-# --output_dir: 结果保存路径
+├── scripts/                   # 工具脚本（数据、回放、可视化、分析）
-# --num_scenarios: 要处理的场景数量
+│   ├── generate_expert_data.py    # 从 Waymo 生成专家 (obs, act) pkl
-python scripts/generate_expert_data.py \
+│   ├── visualize.py              # 可视化统一入口（replay / policy / trajectory）
-    --data_dir data/exp_filtered \
+│   ├── analyze_expert_data.py    # 数据分布分析
-    --output_dir data/training_data \
+│   ├── launch_tensorboard.py     # 启动 TensorBoard
-    --num_scenarios 100 \
+│   ├── README.md                  # 脚本用法说明
-    --start_index 0
+│   └── ...
+├── data/                      # 数据目录（相对路径）
+│   ├── exp_filtered/          # Waymo 场景数据
+│   ├── training_data/        # 专家 pkl 输出（generate_expert_data）
+│   └── trajectories/         # 其他轨迹 pkl（如 expert_dataset 输出）
+├── models/                    # 模型保存目录（相对路径）
+│   ├── bc/                    # BC 模型 (.pt)
+│   └── magail/                # MAGAIL 模型 (*_actor.pth, *_critic.pth)
+├── logs/                      # 训练日志 (TensorBoard)
+│   ├── bc/
+│   └── magail/
+├── train_bc.py                # [根目录] BC 训练
+├── train_magail.py            # [根目录] MAGAIL 训练
+└── README.md
 ```
 
-**生成的 `.pkl` 文件结构**：
+## 路径约定（相对项目根）
-包含一个列表，每个元素是一条车辆轨迹（Trajectory Dictionary）：
-*   `obs`: `(T, 45)` - 观测矩阵。包含 Ego 状态 (5维) + 10辆邻居车相对信息 (40维)。
-*   `acts`: `(T, 2)` - 动作矩阵。`[Steering, Accel]`，归一化到 `[-1, 1]`。
-*   `agent_id`: 车辆 ID。
-*   `scenario_id`: 所属场景 ID。
 
-**内置过滤器**：
+- **数据**：Waymo 场景 `data/exp_filtered`；专家 pkl `data/training_data`；其他轨迹 `data/trajectories`
-脚本会自动过滤掉以下无效车辆：
+- **模型**：BC `models/bc/`，MAGAIL `models/magail/`
-1.  **非道路车辆**：始终在停车场或路外行驶的车辆。
+- **日志**：TensorBoard 写入 `logs/bc/`、`logs/magail/`
-2.  **静态车辆**：全称移动距离小于 5米 且速度从未超过 1m/s 的车辆（作为背景流存在，不收集数据）。
 
----
+所有默认路径均为相对项目根，便于在不同设备上复用。
 
-## 🔍 2. 数据可视化与验证
+## 数据处理流程
 
-### 回放可视化
+从 Waymo Motion 原始数据到本项目训练用专家 pkl，依次为：
-使用 `visualize_replay.py` 直观地观察回放效果，确认车辆行为是否自然，以及过滤逻辑是否生效。
+
+**1) 下载 Waymo Motion（TFRecord）**  
+安装 `gsutil` 并登录 Google 账号后，例如只下载 training_20s：
 
 ```bash
-# 运行可视化
+gsutil -m cp -r "gs://waymo_open_dataset_motion_v_1_2_0/uncompressed/scenario/training_20s" ./waymo/
-# --horizon: 回放的最大步数 (Waymo 场景通常为 90 或 198 步)
-python scripts/visualize_replay.py \
-    --data_dir data/exp_filtered \
-    --start_index 0 \
-    --num_scenarios 1 \
-    --horizon 200
 ```
 
-**观察要点**：
+**2) ScenarioNet Convert（TFRecord → ScenarioNet 场景库）**  
-*   **受控车辆 (Controlled Agents)**：控制台会显示数量（如 `Controlled agents: 2`）。这些是真正产生数据的车辆。
+需安装 ScenarioNet、MetaDrive 及 TensorFlow 2.11、protobuf 3.20；转换时不用 GPU。
-*   **背景车辆**：如果在渲染图中看到其他车（通常是路边停放的），但受控数量很少，说明静态过滤生效了。
-
-### 数据分析
-使用 `analyze_expert_data.py` 查看生成数据的统计分布。
 
 ```bash
-python scripts/analyze_expert_data.py --data_path data/training_data/expert_data_0_100.pkl
+python -m scenarionet.convert_waymo -d data/exp_converted --raw_data_path ./waymo/training_20s --num_workers 64
 ```
 
----
+**3) ScenarioNet Filter（按需筛选场景）**  
+从 convert 得到的场景库中筛掉含红绿灯、天桥等场景，输出到如 `data/exp_filtered`。具体命令以 ScenarioNet 文档为准（Operations → Filter）。
 
-## 🧠 3. 模型训练 (Next Steps)
+**4) 本项目：生成专家 pkl**  
+使用筛选后的场景目录，生成训练用 pkl 到 `data/training_data`：
 
-有了 `data/training_data/` 下的专家数据后，您可以开始训练 MAGAIL 模型。
+```bash
+python scripts/generate_expert_data.py --data_dir data/exp_filtered --output_dir data/training_data --num_scenarios 100 --start_index 0
+```
 
-### 训练流程
+## 核心工作流
-1.  **加载数据**：使用 `dataset/expert_dataset.py` 中的 `ExpertDataset` 类加载 `.pkl` 数据。
-2.  **初始化 MAGAIL**：
-    *   **Generator (Policy)**: 接收观测 `(B, 45)`，输出动作 `(B, 2)`。
-    *   **Discriminator**: 接收状态-动作对 `(s, a)`，判断是专家还是生成器。
-3.  **交互采样**：
-    *   在 `MultiAgentScenarioEnv`（非回放模式）中运行 Policy。
-    *   收集 Policy 生成的轨迹。
-4.  **对抗更新**：
-    *   利用专家数据和 Policy 数据训练 Discriminator。
-    *   利用 Discriminator 的输出作为 Reward (GAIL Reward) 训练 Policy (PPO/TRPO)。
 
-### 推荐配置
+### 1. 数据准备
-*   **Observation**: 45维 (Ego + 10 Neighbors)
+使用 `scripts/generate_expert_data.py` 将 Waymo 数据转换为训练用 `.pkl`，输出到 `data/training_data/`。
-*   **Action**: 2维 Continuous (Steering, Accel)
+
-*   **Horizon**: 200 steps
+```bash
-*   **Batch Size**: 1024+ (多智能体环境下数据量很大)
+python scripts/generate_expert_data.py --data_dir data/exp_filtered --output_dir data/training_data --num_scenarios 100
+```
+
+### 2. 行为克隆 (BC)
+- **训练**：`python train_bc.py`（模型保存到 `models/bc/`，日志到 `logs/bc/`）
+- **可视化**：`python scripts/visualize.py policy --policy_type bc --model_path models/bc/policy_best.pt`
+
+### 3. 多智能体对抗模仿学习 (MAGAIL)
+- **训练**：`python train_magail.py`（模型保存到 `models/magail/`，日志到 `logs/magail/`）
+- **可视化**：`python scripts/visualize.py policy --policy_type magail --model_path models/magail/model_50_actor.pth`
+
+### 4. 可视化统一入口
+可视化统一使用 `scripts/visualize.py`，子命令：`replay`（场景回放）、`policy`（BC/MAGAIL 策略）、`trajectory`（专家轨迹 2D 动画）。详见 [scripts/README.md](scripts/README.md)。
+
+## 文件与模块职责
+
+### 根目录脚本
+- **train_bc.py**：BC 训练，从 `dataset.loader` 加载专家 pkl，模型与日志写入 `models/bc/`、`logs/bc/`
+- **train_magail.py**：MAGAIL 训练，环境使用 `BCScenarioEnv`（45 维），从 `dataset.loader` 加载专家数据，模型与日志写入 `models/magail/`、`logs/magail/`
+
+### Env 模块
+- **Env/bc_env.py**：`BCScenarioEnv`，45 维观测（Ego 5 维 + 10 邻居×4 维），BC 与 MAGAIL 训练/评估共用
+- **Env/scenario_env.py**：`MultiAgentScenarioEnv` 基类，Waymo 场景加载与步进
+- **Env/expert_replay_env.py**：专家轨迹回放与逆动力学动作，供 `generate_expert_data.py` 与回放可视化
+- **Env/inverse_dynamics.py**：轨迹 → 油门/转向动作
+
+### Algorithm 模块
+- **Algorithm/policy.py**：`StateIndependentPolicy`，BC 使用的 MLP 策略
+
+### scripts 目录
+工具脚本用途与用法见 [scripts/README.md](scripts/README.md)。

dataset/expert_dataset.py

@@ -244,6 +244,7 @@ class ExpertTrajectoryDataset(Dataset):
                 print(f"  观测维度: {obs_dim} (应为107)")
         
         if save_path:
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
             with open(save_path, "wb") as f:
                 pickle.dump({
                     "trajectories": all_trajectories,
@@ -282,7 +283,7 @@ if __name__ == "__main__":
         trajectories, observations = ExpertTrajectoryDataset.collect_with_full_obs(
             env_config,
             num_scenarios=10,
-            save_path="./expert_trajectories_full.pkl"
+            save_path="data/trajectories/expert_trajectories_full.pkl"
         )
         
         if len(trajectories) > 0:

dataset/loader.py

@@ -0,0 +1,103 @@
+"""
+统一数据加载：BC/MAGAIL 训练用专家 pkl 的加载函数与 Dataset。
+主训练流水线使用本模块；dataset/expert_dataset.py 为可选 107 维/5 维管线。
+"""
+import os
+import glob
+import pickle
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+def load_expert_pkl(expert_data_path):
+    """从目录或单个 pkl 加载专家 (obs, acts)，返回 concat 后的 obs_data, act_data。"""
+    if os.path.isdir(expert_data_path):
+        pkl_files = glob.glob(os.path.join(expert_data_path, "*.pkl"))
+        if not pkl_files:
+            raise FileNotFoundError(f"No .pkl files in {expert_data_path}")
+        print(f"Found {len(pkl_files)} pickle files in {expert_data_path}")
+    elif os.path.exists(expert_data_path):
+        pkl_files = [expert_data_path]
+    else:
+        raise FileNotFoundError(f"Expert data path not found: {expert_data_path}")
+
+    obs_data, act_data = [], []
+    for pkl_file in pkl_files:
+        try:
+            with open(pkl_file, "rb") as f:
+                data = pickle.load(f)
+            if isinstance(data, list):
+                for traj in data:
+                    if "obs" in traj and "acts" in traj:
+                        obs_data.append(traj["obs"])
+                        act_data.append(traj["acts"])
+            elif isinstance(data, dict):
+                if "observations" in data and "actions" in data:
+                    obs_data.append(data["observations"])
+                    act_data.append(data["actions"])
+            else:
+                print(f"Skipping {pkl_file}: Unknown data format {type(data)}")
+        except Exception as e:
+            print(f"Error loading {pkl_file}: {e}")
+
+    if len(obs_data) == 0:
+        raise ValueError("No valid data loaded from provided path.")
+    obs_data = np.concatenate(obs_data, axis=0)
+    act_data = np.concatenate(act_data, axis=0)
+    print(f"Total loaded samples: {len(obs_data)}")
+    return obs_data, act_data
+
+
+class MAGAILExpertDataset(Dataset):
+    def __init__(self, data_dir, transform=None):
+        """
+        Args:
+            data_dir (str): Directory containing .pkl files from generate_expert_data.py
+            transform (callable, optional): Optional transform to be applied on a sample.
+        """
+        self.data_dir = data_dir
+        self.transform = transform
+        self.trajectories = []
+        self.flat_data = []  # (obs, act) pairs
+
+        # Load all .pkl files
+        pkl_files = glob.glob(os.path.join(data_dir, "*.pkl"))
+        print(f"Loading data from {len(pkl_files)} files in {data_dir}...")
+
+        for pkl_file in pkl_files:
+            try:
+                with open(pkl_file, "rb") as f:
+                    data = pickle.load(f)
+                    # data is a list of dicts: {'obs': (T, 45), 'acts': (T, 2), ...}
+                    self.trajectories.extend(data)
+            except Exception as e:
+                print(f"Error loading {pkl_file}: {e}")
+
+        # Flatten for training Discriminator/BC
+        print(f"Processing {len(self.trajectories)} trajectories...")
+        for traj in self.trajectories:
+            obs = traj["obs"]
+            acts = traj["acts"]
+
+            # obs: (T, 45), acts: (T, 2)
+            for i in range(len(obs)):
+                self.flat_data.append((obs[i], acts[i]))
+
+        print(f"Total samples: {len(self.flat_data)}")
+
+    def __len__(self):
+        return len(self.flat_data)
+
+    def __getitem__(self, idx):
+        obs, act = self.flat_data[idx]
+
+        obs = torch.from_numpy(obs).float()
+        act = torch.from_numpy(act).float()
+
+        sample = {"state": obs, "action": act}
+
+        if self.transform:
+            sample = self.transform(sample)
+
+        return sample

dataset/magail_dataset.py

@@ -1,61 +0,0 @@
-import torch
-from torch.utils.data import Dataset
-import pickle
-import numpy as np
-import os
-import glob
-
-class MAGAILExpertDataset(Dataset):
-    def __init__(self, data_dir, transform=None):
-        """
-        Args:
-            data_dir (str): Directory containing .pkl files from generate_expert_data.py
-            transform (callable, optional): Optional transform to be applied on a sample.
-        """
-        self.data_dir = data_dir
-        self.transform = transform
-        self.trajectories = []
-        self.flat_data = [] # (obs, act) pairs
-        
-        # Load all .pkl files
-        pkl_files = glob.glob(os.path.join(data_dir, "*.pkl"))
-        print(f"Loading data from {len(pkl_files)} files in {data_dir}...")
-        
-        for pkl_file in pkl_files:
-            try:
-                with open(pkl_file, 'rb') as f:
-                    data = pickle.load(f)
-                    # data is a list of dicts: {'obs': (T, 45), 'acts': (T, 2), ...}
-                    self.trajectories.extend(data)
-            except Exception as e:
-                print(f"Error loading {pkl_file}: {e}")
-                
-        # Flatten for training Discriminator/BC
-        print(f"Processing {len(self.trajectories)} trajectories...")
-        for traj in self.trajectories:
-            obs = traj['obs']
-            acts = traj['acts']
-            
-            # obs: (T, 45), acts: (T, 2)
-            # We pair them up
-            for i in range(len(obs)):
-                self.flat_data.append((obs[i], acts[i]))
-                
-        print(f"Total samples: {len(self.flat_data)}")
-        
-    def __len__(self):
-        return len(self.flat_data)
-
-    def __getitem__(self, idx):
-        obs, act = self.flat_data[idx]
-        
-        # Convert to tensor
-        obs = torch.from_numpy(obs).float()
-        act = torch.from_numpy(act).float()
-        
-        sample = {'state': obs, 'action': act}
-        
-        if self.transform:
-            sample = self.transform(sample)
-            
-        return sample

scripts/README.md

@@ -0,0 +1,76 @@
+# scripts 工具脚本说明
+
+本目录包含数据生成、回放、可视化与分析等工具脚本。训练脚本（`train_bc.py`、`train_magail.py`）位于项目根目录。
+
+## 路径约定（相对项目根）
+
+- **数据**：`data/exp_filtered`（Waymo 场景）、`data/training_data`（专家 pkl 输出）
+- **模型**：`models/bc/`（BC）、`models/magail/`（MAGAIL）
+- **日志**：`logs/bc/`、`logs/magail/`（TensorBoard）
+
+---
+
+## 脚本列表与用法
+
+### 数据生成
+
+| 脚本 | 用途 | 用法示例 |
+|------|------|----------|
+| [generate_expert_data.py](generate_expert_data.py) | 从 Waymo 数据生成专家 (obs, act) 的 pkl | `python scripts/generate_expert_data.py --data_dir data/exp_filtered --output_dir data/training_data --num_scenarios 100` |
+
+**常用参数**：`--data_dir`（默认 `data/exp_filtered`）、`--output_dir`（默认 `data/training_data`）、`--start_index`、`--num_scenarios`。
+
+---
+
+### 可视化（统一入口）
+
+| 脚本 | 用途 | 用法示例 |
+|------|------|----------|
+| [visualize.py](visualize.py) | **replay**：场景回放（ExpertReplayEnv）；**policy**：BC/MAGAIL 策略；**trajectory**：专家轨迹 2D 动画 | 见下方 |
+
+**子命令**：
+
+- **replay**（原始专家轨迹回放）：
+```bash
+python scripts/visualize.py replay --data_dir data/exp_filtered --num_scenarios 1 --horizon 500
+```
+
+- **policy**（BC 或 MAGAIL 训练策略）：
+```bash
+python scripts/visualize.py policy --policy_type bc --model_path models/bc/policy_best.pt --data_dir data/exp_filtered --num_scenarios 1
+python scripts/visualize.py policy --policy_type magail --model_path models/magail/model_50_actor.pth --num_scenarios 1 --deterministic
+```
+
+- **trajectory**（专家轨迹 matplotlib 俯视图动画）：
+```bash
+python scripts/visualize.py trajectory --data_dir data/exp_filtered --scenario_idx 0
+```
+
+**公共参数**：`--data_dir`（默认 `data/exp_filtered`）、`--start_index`、`--num_scenarios`、`--horizon`。policy 模式另有 `--policy_type`（auto/bc/magail）、`--model_path`、`--deterministic`（仅 MAGAIL）。
+
+---
+
+### 数据分析与检查
+
+| 脚本 | 用途 | 用法示例 |
+|------|------|----------|
+| [analyze_expert_data.py](analyze_expert_data.py) | 分析专家数据分布与统计 | 见脚本内 `__main__`（依赖 env 与数据目录配置） |
+| [check_track_fields.py](check_track_fields.py) | 检查 Waymo 轨迹字段 | 见脚本内 `__main__` |
+| [check_database_info.py](check_database_info.py) | 检查数据库/场景信息 | 见脚本内 `__main__`（含硬编码路径，可按需改为 `data/exp_filtered`） |
+
+---
+
+### 其他
+
+| 脚本 | 用途 | 用法示例 |
+|------|------|----------|
+| [launch_tensorboard.py](launch_tensorboard.py) | 启动 TensorBoard | `python scripts/launch_tensorboard.py --logdir logs`（或 `logs/bc` / `logs/magail`） |
+
+---
+
+## 与训练流程的对应关系
+
+1. **数据准备**：`generate_expert_data.py` → 输出到 `data/training_data/*.pkl`
+2. **BC 训练**：根目录 `train_bc.py` → 模型保存到 `models/bc/`，日志到 `logs/bc/`
+3. **MAGAIL 训练**：根目录 `train_magail.py` → 模型保存到 `models/magail/`，日志到 `logs/magail/`
+4. **可视化**：`scripts/visualize.py`（子命令 replay / policy / trajectory）→ 数据目录默认 `data/exp_filtered`

scripts/generate_expert_data.py

@@ -153,8 +153,8 @@ def generate_data(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--data_dir", type=str, default="/home/huangfukk/MAGAIL4AutoDrive/data/exp_filtered", help="Path to Waymo pickles (or filtered index)")
+    parser.add_argument("--data_dir", type=str, default="data/exp_filtered", help="Path to Waymo pickles (or filtered index)")
-    parser.add_argument("--output_dir", type=str, default="/home/huangfukk/MAGAIL4AutoDrive/data/training", help="Output directory")
+    parser.add_argument("--output_dir", type=str, default="data/training_data", help="Output directory")
     parser.add_argument("--start_index", type=int, default=0)
     parser.add_argument("--num_scenarios", type=int, default=10)
     

scripts/launch_tensorboard.py

@@ -0,0 +1,18 @@
+import sys
+import types
+import os
+
+# Mock imghdr module for Python 3.13 compatibility
+# TensorBoard depends on imghdr which was removed in Python 3.13
+if sys.version_info >= (3, 13):
+    if 'imghdr' not in sys.modules:
+        imghdr_mock = types.ModuleType('imghdr')
+        imghdr_mock.what = lambda filename, h=None: None
+        # Mock tests list which tensorboard appends to
+        imghdr_mock.tests = []
+        sys.modules['imghdr'] = imghdr_mock
+
+from tensorboard import main as tb_main
+
+if __name__ == '__main__':
+    sys.exit(tb_main.run_main())

scripts/visualize.py

@@ -0,0 +1,395 @@
+"""
+Unified visualization: replay (scenario replay), policy (BC/MAGAIL), trajectory (2D expert trajectory animation).
+Usage: python scripts/visualize.py <replay|policy|trajectory> [args...]
+"""
+import argparse
+import os
+import sys
+import time
+import numpy as np
+import torch
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+# --- Replay ---
+def _run_replay(args):
+    from Env.expert_replay_env import ExpertReplayEnv
+
+    data_path = os.path.abspath(args.data_dir)
+    if not os.path.exists(data_path):
+        raise ValueError(f"Data directory {data_path} not found")
+
+    from metadrive.scenario.utils import read_dataset_summary
+    _, summary_lookup, _ = read_dataset_summary(data_path)
+    if args.start_index >= len(summary_lookup):
+        raise ValueError(
+            f"start_index={args.start_index} out of range. Dataset has {len(summary_lookup)} scenarios."
+        )
+    max_available = len(summary_lookup) - args.start_index
+    num_to_run = min(args.num_scenarios, max_available)
+
+    env_config = {
+        "data_directory": data_path,
+        "is_multi_agent": True,
+        "num_controlled_agents": 100,
+        "horizon": args.horizon,
+        "use_render": True,
+        "sequential_seed": True,
+        "reactive_traffic": False,
+        "start_scenario_index": args.start_index,
+        "num_scenarios": -1,
+        "log_level": 40,
+    }
+
+    print(f"Initializing ExpertReplayEnv with data from {data_path}...")
+    env = ExpertReplayEnv(config=env_config)
+
+    try:
+        for i in range(args.start_index, args.start_index + num_to_run):
+            print(f"\n--- Playing Scenario {i} ---")
+            try:
+                obs = env.reset(seed=i)
+            except Exception as e:
+                print(f"Error resetting scenario {i}: {e}")
+                continue
+
+            print(f"Scenario loaded. Controlled agents: {len(env.controlled_agents)}")
+
+            for step in range(args.horizon):
+                obs, rewards, dones, infos = env.step(None)
+                env.render(
+                    mode="top_down",
+                    text={"Step": step, "Agents": len(env.controlled_agents), "Scenario": i},
+                )
+                time.sleep(0.05)
+                if dones["__all__"]:
+                    print(f"Scenario {i} finished at step {step}")
+                    break
+    except KeyboardInterrupt:
+        print("Interrupted by user")
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        print(f"Global error: {e}")
+    finally:
+        env.close()
+        print("Environment closed.")
+
+
+# --- Policy (BC / MAGAIL) ---
+def _resolve_data_dir(data_dir_arg):
+    if data_dir_arg:
+        data_dir = data_dir_arg
+    else:
+        data_dir = os.path.join(project_root, "data", "exp_filtered")
+        if not os.path.exists(data_dir):
+            data_dir = os.path.join(project_root, "data", "exp_converted")
+    if not os.path.exists(data_dir):
+        raise FileNotFoundError(f"Data directory not found at {data_dir}. Please specify --data_dir.")
+    return data_dir
+
+
+def _resolve_model_path(model_path, policy_type):
+    if os.path.exists(model_path):
+        return model_path
+    if policy_type == "bc":
+        candidate = os.path.join(project_root, "models", "bc", os.path.basename(model_path))
+    else:
+        candidate = os.path.join(project_root, "models", "magail", os.path.basename(model_path))
+    if os.path.exists(candidate):
+        return candidate
+    if policy_type == "magail" and not model_path.endswith("_actor.pth"):
+        candidate = os.path.join(project_root, "models", "magail", os.path.basename(model_path) + "_actor.pth")
+        if os.path.exists(candidate):
+            return candidate
+    raise FileNotFoundError(f"Model path {model_path} not found.")
+
+
+def _run_policy(args):
+    from Env.bc_env import BCScenarioEnv
+    from metadrive.engine.engine_utils import close_engine
+
+    policy_type = (args.policy_type or "auto").lower()
+    if policy_type == "auto":
+        policy_type = "bc" if args.model_path.endswith(".pt") else "magail"
+
+    data_dir = _resolve_data_dir(args.data_dir)
+    data_path = os.path.abspath(data_dir)
+    env_config = {
+        "data_directory": data_path,
+        "is_multi_agent": True,
+        "num_controlled_agents": 3,
+        "horizon": args.horizon,
+        "use_render": True,
+        "sequential_seed": True,
+        "start_scenario_index": args.start_index,
+        "num_scenarios": args.num_scenarios,
+        "log_level": 40,
+    }
+
+    print(f"Initializing BCScenarioEnv (policy_type={policy_type})...")
+    try:
+        env = BCScenarioEnv(env_config, agent2policy={})
+    except Exception as e:
+        print(f"Error init env: {e}. Trying to close lingering engine...")
+        try:
+            close_engine()
+        except Exception:
+            pass
+        env = BCScenarioEnv(env_config, agent2policy={})
+
+    state_dim = 45
+    action_dim = 2
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model_path = _resolve_model_path(args.model_path, policy_type)
+    print(f"Loading model from {model_path}...")
+
+    if policy_type == "bc":
+        from Algorithm.policy import StateIndependentPolicy
+        policy = StateIndependentPolicy(
+            state_shape=(state_dim,),
+            action_shape=(action_dim,),
+            hidden_units=(256, 256),
+            hidden_activation=torch.nn.Tanh(),
+        ).to(device)
+        policy.load_state_dict(torch.load(model_path, map_location=device))
+        policy.eval()
+    else:
+        from train_magail import Actor
+        actor = Actor(state_dim, action_dim).to(device)
+        actor.load_state_dict(torch.load(model_path, map_location=device))
+        actor.eval()
+
+    try:
+        for i in range(args.start_index, args.start_index + args.num_scenarios):
+            print(f"\n--- Playing Scenario {i} ---")
+            try:
+                obs_dict = env.reset(seed=i)
+            except Exception as e:
+                print(f"Error resetting {i}: {e}. Skipping.")
+                try:
+                    close_engine()
+                    env = BCScenarioEnv(env_config, agent2policy={})
+                except Exception:
+                    pass
+                continue
+
+            print(f"Scenario loaded. Controlled agents: {len(obs_dict)}")
+            step_count = 0
+            episode_reward = 0.0
+
+            while True:
+                agent_ids = list(obs_dict.keys())
+                obs_list = [obs_dict[aid] for aid in agent_ids]
+                obs_tensor = torch.FloatTensor(np.array(obs_list)).to(device)
+
+                with torch.no_grad():
+                    if policy_type == "bc":
+                        actions_np = policy(obs_tensor).cpu().numpy()
+                    else:
+                        dist = actor(obs_tensor)
+                        if args.deterministic:
+                            actions_np = torch.tanh(dist.mean).cpu().numpy()
+                        else:
+                            actions_np = torch.tanh(dist.sample()).cpu().numpy()
+
+                actions = {aid: actions_np[idx].flatten() for idx, aid in enumerate(agent_ids)}
+                obs_dict, rewards, dones, infos = env.step(actions)
+                episode_reward += sum(rewards.values())
+
+                env.render(
+                    mode="top_down",
+                    text={
+                        "Scenario": i,
+                        "Step": step_count,
+                        "Agents": len(obs_dict),
+                        "Total Reward": f"{episode_reward:.2f}",
+                    },
+                )
+                step_count += 1
+
+                if dones["__all__"] or step_count >= args.horizon:
+                    print(f"Scenario finished at step {step_count}, reward {episode_reward:.2f}")
+                    break
+    except KeyboardInterrupt:
+        print("Interrupted.")
+    finally:
+        env.close()
+
+
+# --- Trajectory (matplotlib 2D animation) ---
+def _build_expert_trajectories_from_env(env):
+    """Build expert_trajectories dict from env (ExpertReplayEnv has traffic_manager.current_traffic_data)."""
+    if hasattr(env, "expert_trajectories") and env.expert_trajectories:
+        return env.expert_trajectories
+    if not hasattr(env, "engine") or not hasattr(env.engine, "traffic_manager"):
+        return {}
+    from metadrive.type import MetaDriveType
+    data = getattr(env.engine.traffic_manager, "current_traffic_data", None)
+    if not data:
+        return {}
+    expert_trajs = {}
+    for scenario_id, track in data.items():
+        if track.get("type") != MetaDriveType.VEHICLE or "state" not in track:
+            continue
+        state = track["state"]
+        positions = state.get("position")
+        if positions is None:
+            continue
+        valid = state.get("valid", np.ones(len(positions), dtype=bool))
+        valid = np.asarray(valid).flatten()
+        if valid.size != len(positions):
+            valid = np.ones(len(positions), dtype=bool)
+        first_show = int(np.argmax(valid)) if valid.any() else 0
+        last_show = len(valid) - 1 - int(np.argmax(valid[::-1])) if valid.any() else len(positions) - 1
+        obj_id = track.get("metadata", {}).get("object_id", str(scenario_id))
+        expert_trajs[obj_id] = {
+            "positions": np.asarray(positions),
+            "start_timestep": first_show,
+            "end_timestep": last_show,
+        }
+    return expert_trajs
+
+
+def _run_trajectory_animation(expert_trajs, scenario_idx):
+    import matplotlib.pyplot as plt
+    from matplotlib.animation import FuncAnimation
+
+    if len(expert_trajs) == 0:
+        print("No expert trajectories to visualize.")
+        return
+
+    fig, ax = plt.subplots(figsize=(12, 12))
+    max_timestep = max(t["end_timestep"] for t in expert_trajs.values())
+    min_timestep = min(t["start_timestep"] for t in expert_trajs.values())
+
+    colors = plt.cm.tab10(np.linspace(0, 1, len(expert_trajs)))
+    for idx, (obj_id, traj) in enumerate(expert_trajs.items()):
+        positions = np.asarray(traj["positions"])
+        if positions.ndim >= 2:
+            positions = positions[:, :2]
+        else:
+            continue
+        ax.plot(
+            positions[:, 0], positions[:, 1],
+            color=colors[idx], alpha=0.3, linewidth=1,
+            label=f"Vehicle {str(obj_id)[:6]}",
+        )
+
+    scatter = ax.scatter([], [], s=200, c="red", marker="o", edgecolors="black", linewidths=2)
+    time_text = ax.text(0.02, 0.95, "", transform=ax.transAxes, fontsize=14)
+    ax.set_xlabel("X (m)")
+    ax.set_ylabel("Y (m)")
+    ax.set_title(f"Expert Trajectory Visualization - Scenario {scenario_idx}")
+    ax.legend(loc="upper right", fontsize=8)
+    ax.grid(True, alpha=0.3)
+    ax.axis("equal")
+
+    def update(frame):
+        current_time = min_timestep + frame
+        current_positions = []
+        for traj in expert_trajs.values():
+            st, et = traj["start_timestep"], traj["end_timestep"]
+            if st <= current_time <= et:
+                pos = np.asarray(traj["positions"])
+                if pos.ndim >= 2:
+                    pos = pos[current_time - st, :2]
+                else:
+                    continue
+                current_positions.append(pos)
+        if current_positions:
+            scatter.set_offsets(np.array(current_positions))
+        time_text.set_text(f"Time: {frame * 0.1:.1f}s (Frame {frame})")
+        return scatter, time_text
+
+    anim = FuncAnimation(
+        fig, update, frames=max_timestep - min_timestep + 1,
+        interval=100, blit=True, repeat=True,
+    )
+    plt.tight_layout()
+    plt.show()
+    return anim
+
+
+def _run_trajectory(args):
+    from Env.expert_replay_env import ExpertReplayEnv
+
+    data_dir = _resolve_data_dir(args.data_dir)
+    data_path = os.path.abspath(data_dir)
+    env_config = {
+        "data_directory": data_path,
+        "is_multi_agent": True,
+        "num_controlled_agents": 100,
+        "horizon": 500,
+        "use_render": False,
+        "sequential_seed": True,
+        "reactive_traffic": False,
+        "start_scenario_index": args.scenario_idx,
+        "num_scenarios": 1,
+        "log_level": 40,
+    }
+
+    env = ExpertReplayEnv(config=env_config)
+    try:
+        env.reset(seed=args.scenario_idx)
+        expert_trajs = _build_expert_trajectories_from_env(env)
+        _run_trajectory_animation(expert_trajs, args.scenario_idx)
+    finally:
+        env.close()
+
+
+# --- Main ---
+def main():
+    parser = argparse.ArgumentParser(
+        description="Unified visualization: replay, policy (BC/MAGAIL), trajectory.",
+    )
+    subparsers = parser.add_subparsers(dest="mode", required=True, help="replay | policy | trajectory")
+
+    # Common args for data_dir (used by all)
+    def add_common_data_args(p):
+        p.add_argument("--data_dir", type=str, default="data/exp_filtered", help="Waymo scenario directory")
+        p.add_argument("--start_index", type=int, default=0)
+        p.add_argument("--num_scenarios", type=int, default=1)
+        p.add_argument("--horizon", type=int, default=200)
+
+    # replay
+    pr = subparsers.add_parser("replay", help="Replay scenario with ExpertReplayEnv (no policy)")
+    add_common_data_args(pr)
+    pr.set_defaults(horizon=500)
+
+    # policy
+    pp = subparsers.add_parser("policy", help="Visualize BC or MAGAIL trained policy")
+    add_common_data_args(pp)
+    pp.add_argument("--policy_type", type=str, default="auto", choices=["auto", "bc", "magail"])
+    pp.add_argument("--model_path", type=str, default="models/bc/policy_best.pt")
+    pp.add_argument("--deterministic", action="store_true", help="MAGAIL: use mean action")
+
+    # trajectory
+    pt = subparsers.add_parser("trajectory", help="2D matplotlib animation of expert trajectories")
+    pt.add_argument("--data_dir", type=str, default="data/exp_filtered")
+    pt.add_argument("--scenario_idx", type=int, default=0)
+
+    args = parser.parse_args()
+
+    # Resolve data_dir relative to project root when default
+    if args.mode != "trajectory":
+        if args.data_dir in ("data/exp_filtered", "data/exp_converted"):
+            args.data_dir = os.path.join(project_root, args.data_dir)
+    else:
+        if args.data_dir in ("data/exp_filtered", "data/exp_converted"):
+            args.data_dir = os.path.join(project_root, args.data_dir)
+
+    if args.mode == "replay":
+        _run_replay(args)
+    elif args.mode == "policy":
+        _run_policy(args)
+    elif args.mode == "trajectory":
+        _run_trajectory(args)
+    else:
+        parser.error(f"Unknown mode: {args.mode}")
+
+
+if __name__ == "__main__":
+    main()

scripts/visualize_expert_trajectory.py

@@ -1,105 +0,0 @@
-import sys
-import os
-
-# 添加路径
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-env_dir = os.path.join(project_root, "Env")
-sys.path.insert(0, project_root)
-sys.path.insert(0, env_dir)
-
-# 现在可以导入了
-from scenario_env import MultiAgentScenarioEnv
-from metadrive.engine.asset_loader import AssetLoader
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.animation import FuncAnimation
-
-class DummyPolicy:
-    """
-    占位策略,用于数据检查时初始化环境
-    不需要实际执行动作,只是为了满足环境初始化要求
-    """
-    def act(self, *args, **kwargs):
-        # 返回零动作 [throttle, steering]
-        return np.array([0.0, 0.0])
-
-def visualize_expert_trajectory(env, scenario_idx=0):
-    """
-    可视化专家轨迹的俯视图动画
-    """
-    env.reset()
-    expert_trajs = env.expert_trajectories
-    
-    if len(expert_trajs) == 0:
-        print("当前场景无专家轨迹")
-        return
-    
-    # 设置绘图
-    fig, ax = plt.subplots(figsize=(12, 12))
-    
-    # 获取所有轨迹的最大时间长度
-    max_timestep = max(traj["end_timestep"] for traj in expert_trajs.values())
-    min_timestep = min(traj["start_timestep"] for traj in expert_trajs.values())
-    
-    # 绘制完整轨迹(淡色)
-    colors = plt.cm.tab10(np.linspace(0, 1, len(expert_trajs)))
-    for idx, (obj_id, traj) in enumerate(expert_trajs.items()):
-        positions = traj["positions"][:, :2]
-        ax.plot(positions[:, 0], positions[:, 1], 
-                color=colors[idx], alpha=0.3, linewidth=1,
-                label=f'Vehicle {obj_id[:6]}')
-    
-    # 初始化当前位置标记
-    scatter = ax.scatter([], [], s=200, c='red', marker='o', edgecolors='black', linewidths=2)
-    time_text = ax.text(0.02, 0.95, '', transform=ax.transAxes, fontsize=14)
-    
-    ax.set_xlabel('X (m)')
-    ax.set_ylabel('Y (m)')
-    ax.set_title(f'Expert Trajectory Visualization - Scenario {scenario_idx}')
-    ax.legend(loc='upper right', fontsize=8)
-    ax.grid(True, alpha=0.3)
-    ax.axis('equal')
-    
-    def update(frame):
-        current_time = min_timestep + frame
-        
-        # 收集当前时间所有车辆的位置
-        current_positions = []
-        for traj in expert_trajs.values():
-            if traj["start_timestep"] <= current_time <= traj["end_timestep"]:
-                idx = current_time - traj["start_timestep"]
-                pos = traj["positions"][idx, :2]
-                current_positions.append(pos)
-        
-        if len(current_positions) > 0:
-            current_positions = np.array(current_positions)
-            scatter.set_offsets(current_positions)
-        
-        time_text.set_text(f'Time: {frame * 0.1:.1f}s (Frame {frame})')
-        return scatter, time_text
-    
-    anim = FuncAnimation(fig, update, frames=max_timestep-min_timestep+1,
-                        interval=100, blit=True, repeat=True)
-    
-    plt.tight_layout()
-    plt.show()
-    
-    return anim
-
-if __name__ == "__main__":
-    WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
-    data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
-    
-    env = MultiAgentScenarioEnv(
-        config={
-            "data_directory": data_dir,
-            "is_multi_agent": True,
-            "num_controlled_agents": 3,
-            "use_render": False,
-        },
-        agent2policy=DummyPolicy()
-    )
-    
-    # 可视化第一个场景
-    anim = visualize_expert_trajectory(env, scenario_idx=0)

scripts/visualize_replay.py

@@ -1,93 +0,0 @@
-import argparse
-import os
-import sys
-import time
-
-# Add project root to Python path so we can import Env module
-project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-if project_root not in sys.path:
-    sys.path.insert(0, project_root)
-
-from Env.expert_replay_env import ExpertReplayEnv
-
-def visualize_replay(args):
-    data_path = os.path.abspath(args.data_dir)
-    if not os.path.exists(data_path):
-        raise ValueError(f"Data directory {data_path} not found")
-
-    # Same as data generation: avoid MetaDrive assertion when requested num_scenarios > available.
-    from metadrive.scenario.utils import read_dataset_summary
-    _, summary_lookup, _ = read_dataset_summary(data_path)
-    if args.start_index >= len(summary_lookup):
-        raise ValueError(
-            f"start_index={args.start_index} out of range. Dataset has {len(summary_lookup)} scenarios."
-        )
-    max_available = len(summary_lookup) - args.start_index
-    num_to_run = min(args.num_scenarios, max_available)
-
-    env_config = {
-        "data_directory": data_path,
-        "is_multi_agent": True,
-        "num_controlled_agents": 100,
-        "horizon": args.horizon,
-        "use_render": True,  # Enable rendering
-        "sequential_seed": True,
-        "reactive_traffic": False,
-        "start_scenario_index": args.start_index,
-        "num_scenarios": -1,
-        "log_level": 40, # ERROR
-        # "pstats": True, # For performance debugging
-    }
-
-    print(f"Initializing ExpertReplayEnv with data from {data_path}...")
-    env = ExpertReplayEnv(config=env_config)
-
-    try:
-        for i in range(args.start_index, args.start_index + num_to_run):
-            print(f"\n--- Playing Scenario {i} ---")
-            try:
-                obs = env.reset(seed=i)
-            except Exception as e:
-                print(f"Error resetting scenario {i}: {e}")
-                continue
-            
-            print(f"Scenario loaded. Controlled agents: {len(env.controlled_agents)}")
-            
-            for step in range(args.horizon):
-                # Step
-                obs, rewards, dones, infos = env.step(None)
-                
-                # Render
-                env.render(mode="top_down", 
-                           text={
-                               "Step": step, 
-                               "Agents": len(env.controlled_agents),
-                               "Scenario": i
-                           })
-                
-                # Sleep to control playback speed
-                time.sleep(0.05)
-                
-                if dones["__all__"]:
-                    print(f"Scenario {i} finished at step {step}")
-                    break
-                    
-    except KeyboardInterrupt:
-        print("Interrupted by user")
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-        print(f"Global error: {e}")
-    finally:
-        env.close()
-        print("Environment closed.")
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data_dir", type=str, default="/home/huangfukk/MAGAIL4AutoDrive/data/exp_filtered", help="Path to Waymo data")
-    parser.add_argument("--start_index", type=int, default=0)
-    parser.add_argument("--num_scenarios", type=int, default=1)
-    parser.add_argument("--horizon", type=int, default=500)
-    
-    args = parser.parse_args()
-    visualize_replay(args)

train_bc.py

@@ -0,0 +1,146 @@
+"""
+BC 训练脚本：负责数据加载、环境评估、日志与保存；BC 算法由 Algorithm.bc 提供。
+使用方式不变：python train_bc.py [--expert_data_path data/training_data] [--save_dir models/bc] ...
+"""
+import os
+import numpy as np
+import torch
+import argparse
+from torch.utils.data import DataLoader, TensorDataset
+from torch.optim import Adam
+from torch.optim.lr_scheduler import ExponentialLR
+from datetime import datetime
+from torch.utils.tensorboard import SummaryWriter
+
+from Algorithm.policy import StateIndependentPolicy
+from Algorithm.bc import train_bc_epoch, eval_bc_epoch
+from Env.bc_env import BCScenarioEnv
+from dataset.loader import load_expert_pkl
+
+
+def evaluate_policy(policy, args, device):
+    """在 BCScenarioEnv 中评估策略，跑若干 episode，返回平均 reward。"""
+    waymo_data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+    data_dir = os.path.join(waymo_data_dir, "exp_filtered")
+    if not os.path.exists(data_dir):
+        data_dir = os.path.join(waymo_data_dir, "exp_converted")
+    if not os.path.exists(data_dir):
+        print(f"[ERROR] Could not find scenario data in {waymo_data_dir}. Evaluation skipped.")
+        return 0.0
+
+    env_config = {
+        "data_directory": data_dir,
+        "is_multi_agent": True,
+        "num_controlled_agents": 3,
+        "use_render": False,
+        "sequential_seed": True,
+        "horizon": 200,
+    }
+    env = BCScenarioEnv(env_config, agent2policy=None)
+    total_rewards = []
+
+    try:
+        for i in range(3):
+            obs_dict = env.reset(seed=i)
+            episode_reward = 0
+            dones = {"__all__": False}
+            step_count = 0
+            horizon = 200
+            while not dones["__all__"]:
+                step_count += 1
+                if step_count >= horizon:
+                    break
+                if not obs_dict:
+                    obs_dict, _, dones, _ = env.step({})
+                    continue
+                agent_ids = list(obs_dict.keys())
+                obs_list = [obs_dict[aid] for aid in agent_ids]
+                obs_tensor = torch.FloatTensor(np.array(obs_list)).to(device)
+                with torch.no_grad():
+                    actions, _ = policy.sample(obs_tensor)
+                    actions = actions.cpu().numpy()
+                action_dict = {aid: act for aid, act in zip(agent_ids, actions)}
+                obs_dict, rewards, dones, _ = env.step(action_dict)
+                episode_reward += sum(rewards.values())
+            total_rewards.append(episode_reward)
+            print(f"  Eval Episode {i}: Total Reward {episode_reward:.2f}")
+        avg_reward = float(np.mean(total_rewards))
+        print(f"  Average Evaluation Reward: {avg_reward:.2f}")
+        return avg_reward
+    except Exception as e:
+        print(f"Evaluation failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return 0.0
+    finally:
+        env.close()
+
+
+def main(args):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    os.makedirs("logs/bc", exist_ok=True)
+    log_dir = os.path.join("logs", "bc", datetime.now().strftime("%Y%m%d-%H%M%S"))
+    writer = SummaryWriter(log_dir)
+    print(f"TensorBoard logging to: {log_dir}")
+    os.makedirs(args.save_dir, exist_ok=True)
+
+    obs_data, act_data = load_expert_pkl(args.expert_data_path)
+    obs_tensor = torch.FloatTensor(obs_data)
+    act_tensor = torch.FloatTensor(act_data)
+    dataset = TensorDataset(obs_tensor, act_tensor)
+    train_size = int(0.8 * len(dataset))
+    val_size = len(dataset) - train_size
+    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
+    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
+    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False)
+    print(f"Dataset loaded. Train size: {len(train_dataset)}, Val size: {len(val_dataset)}")
+
+    state_dim = obs_data.shape[1]
+    action_dim = act_data.shape[1]
+    print(f"State Dim: {state_dim}, Action Dim: {action_dim}")
+
+    policy = StateIndependentPolicy(
+        state_shape=(state_dim,),
+        action_shape=(action_dim,),
+        hidden_units=(256, 256),
+        hidden_activation=torch.nn.Tanh(),
+    ).to(device)
+    optimizer = Adam(policy.parameters(), lr=args.lr)
+    scheduler = ExponentialLR(optimizer, gamma=0.99)
+
+    best_val_loss = float("inf")
+    for epoch in range(args.epochs):
+        avg_train_loss = train_bc_epoch(policy, train_loader, optimizer, device)
+        scheduler.step()
+        avg_val_loss = eval_bc_epoch(policy, val_loader, device)
+
+        print(f"Epoch {epoch+1}/{args.epochs} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}")
+        writer.add_scalar("Loss/train", avg_train_loss, epoch)
+        writer.add_scalar("Loss/val", avg_val_loss, epoch)
+        writer.add_scalar("Learning_rate", scheduler.get_last_lr()[0], epoch)
+
+        if avg_val_loss < best_val_loss:
+            best_val_loss = avg_val_loss
+            torch.save(policy.state_dict(), os.path.join(args.save_dir, "policy_best.pt"))
+
+        if (epoch + 1) % args.eval_freq == 0:
+            eval_reward = evaluate_policy(policy, args, device)
+            writer.add_scalar("Reward/eval", eval_reward, epoch)
+
+    torch.save(policy.state_dict(), os.path.join(args.save_dir, "policy_final.pt"))
+    writer.close()
+    print("Training finished.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--expert_data_path", type=str, default="data/training_data", help="Path to expert data pickle or directory")
+    parser.add_argument("--save_dir", type=str, default="models/bc", help="Directory to save models")
+    parser.add_argument("--epochs", type=int, default=100)
+    parser.add_argument("--batch_size", type=int, default=64)
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--eval_freq", type=int, default=10)
+    args = parser.parse_args()
+    main(args)

train_magail.py

@@ -9,7 +9,8 @@ import argparse
 import signal
 import sys
 from torch.utils.data import DataLoader
-from dataset.magail_dataset import MAGAILExpertDataset
+from dataset.loader import MAGAILExpertDataset
+from Env.bc_env import BCScenarioEnv
 
 # --- Networks ---
 
@@ -79,18 +80,30 @@ class PPO:
         self.K_epochs = K_epochs
         self.mse_loss = nn.MSELoss()
 
+    def _log_prob_from_dist(self, dist, pre_tanh_action):
+        # Tanh-squashed Gaussian log-prob with correction term.
+        log_prob = dist.log_prob(pre_tanh_action)
+        correction = torch.log(1 - torch.tanh(pre_tanh_action) ** 2 + 1e-6)
+        return (log_prob - correction).sum(dim=-1)
+
     def select_action(self, state):
         with torch.no_grad():
             state = torch.FloatTensor(state).cuda()
             dist = self.actor(state)
-            action = dist.sample()
+            pre_tanh_action = dist.sample()
-            action_logprob = dist.log_prob(action).sum(dim=-1)
+            action = torch.tanh(pre_tanh_action)
-        return action.cpu().numpy(), action_logprob.cpu().numpy()
+            action_logprob = self._log_prob_from_dist(dist, pre_tanh_action)
+        return (
+            action.cpu().numpy(),
+            action_logprob.cpu().numpy(),
+            pre_tanh_action.cpu().numpy()
+        )
 
     def update(self, memory):
         # Convert memory to tensors
         states = torch.FloatTensor(np.array(memory['states'])).cuda()
         actions = torch.FloatTensor(np.array(memory['actions'])).cuda()
+        pre_tanh_actions = torch.FloatTensor(np.array(memory['pre_tanh_actions'])).cuda()
         logprobs = torch.FloatTensor(np.array(memory['logprobs'])).cuda()
         rewards = torch.FloatTensor(np.array(memory['rewards'])).cuda()
         next_states = torch.FloatTensor(np.array(memory['next_states'])).cuda()
@@ -124,7 +137,7 @@ class PPO:
         for _ in range(self.K_epochs):
             # Evaluating old actions and values :
             dist = self.actor(states)
-            action_logprobs = dist.log_prob(actions).sum(dim=-1)
+            action_logprobs = self._log_prob_from_dist(dist, pre_tanh_actions)
             dist_entropy = dist.entropy().sum(dim=-1)
             state_values = self.critic(states).squeeze()
             
@@ -152,12 +165,7 @@ class PPO:
 # --- Training Loop ---
 
 def train(args):
-    # 1. Setup Environment (Dummy for now, usually you run simulation here)
+    # 1. Setup Environment (45-dim obs via BCScenarioEnv)
-    # But for MAGAIL we need to collect generated trajectories.
-    # We need the Env class to be importable.
-    from Env.scenario_env import MultiAgentScenarioEnv
-    from Env.simple_idm_policy import ConstantVelocityPolicy # Just for init
-    
     # Config for Env
     env_config = {
         "data_directory": args.data_dir,
@@ -200,12 +208,7 @@ def train(args):
                 yield batch
     expert_iter = cycle(expert_loader)
     
-    # 4. Initialize Env
+    # 4. Initialize Env (BCScenarioEnv provides 45-dim obs)
-    from Env.expert_replay_env import ExpertReplayEnv # Using ReplayEnv for config, but we need ScenarioEnv for simulation?
-    # Actually we need MultiAgentScenarioEnv for interactive training, not Replay.
-    from Env.scenario_env import MultiAgentScenarioEnv
-    from Env.simple_idm_policy import ConstantVelocityPolicy # Placeholder policy for init
-    
     # 2. Setup Models
     # Determine state dim from environment if possible, or use fixed
     # Expert data has 45 dim? 
@@ -220,48 +223,48 @@ def train(args):
     # We need to inject that same logic into the training env, OR
     # subclass MultiAgentScenarioEnv in the training script to override observation.
     
-    class MAGAILScenarioEnv(MultiAgentScenarioEnv):
+    # class MAGAILScenarioEnv(MultiAgentScenarioEnv):
-        def _get_all_obs(self):
+    #     def _get_all_obs(self):
-            # Same logic as ExpertReplayEnv to ensure compatibility
+    #         # Same logic as ExpertReplayEnv to ensure compatibility
-            obs_dict = {}
+    #         obs_dict = {}
-            for agent_id, vehicle in self.controlled_agents.items():
+    #         for agent_id, vehicle in self.controlled_agents.items():
-                # 1. Ego State
+    #             # 1. Ego State
-                ego_state = [
+    #             ego_state = [
-                    vehicle.position[0], vehicle.position[1],
+    #                 vehicle.position[0], vehicle.position[1],
-                    vehicle.velocity[0], vehicle.velocity[1],
+    #                 vehicle.velocity[0], vehicle.velocity[1],
-                    vehicle.heading_theta
+    #                 vehicle.heading_theta
-                ]
+    #             ]
-                
+    #             
-                # 2. Neighbors
+    #             # 2. Neighbors
-                candidates = []
+    #             candidates = []
-                for other_id, other_vehicle in self.engine.agent_manager.active_agents.items():
+    #             for other_id, other_vehicle in self.engine.agent_manager.active_agents.items():
-                    if other_id == agent_id:
+    #                 if other_id == agent_id:
-                        continue
+    #                     continue
-                    dist = np.linalg.norm(vehicle.position - other_vehicle.position)
+    #                 dist = np.linalg.norm(vehicle.position - other_vehicle.position)
-                    if dist < 30.0:
+    #                 if dist < 30.0:
-                        candidates.append((dist, other_vehicle))
+    #                     candidates.append((dist, other_vehicle))
-                
+    #             
-                candidates.sort(key=lambda x: x[0])
+    #             candidates.sort(key=lambda x: x[0])
-                top_10 = candidates[:10]
+    #             top_10 = candidates[:10]
-                
+    #             
-                neighbor_feats = []
+    #             neighbor_feats = []
-                for _, neighbor in top_10:
+    #             for _, neighbor in top_10:
-                    neighbor_feats.extend([
+    #                 neighbor_feats.extend([
-                        neighbor.position[0] - vehicle.position[0], 
+    #                     neighbor.position[0] - vehicle.position[0], 
-                        neighbor.position[1] - vehicle.position[1],
+    #                     neighbor.position[1] - vehicle.position[1],
-                        neighbor.velocity[0], 
+    #                     neighbor.velocity[0], 
-                        neighbor.velocity[1]
+    #                     neighbor.velocity[1]
-                    ])
+    #                 ])
-                    
+    #                 
-                missing = 10 - len(top_10)
+    #             missing = 10 - len(top_10)
-                if missing > 0:
+    #             if missing > 0:
-                    neighbor_feats.extend([0.0] * (4 * missing))
+    #                 neighbor_feats.extend([0.0] * (4 * missing))
-                    
+    #                 
-                obs = np.array(ego_state + neighbor_feats, dtype=np.float32)
+    #             obs = np.array(ego_state + neighbor_feats, dtype=np.float32)
-                obs_dict[agent_id] = obs
+    #             obs_dict[agent_id] = obs
-            return obs_dict
+    #         return obs_dict
-
+    
-    env = MAGAILScenarioEnv(config=env_config, agent2policy={}) # Pass empty dict if we control all externally
+    env = BCScenarioEnv(env_config, agent2policy={})  # 45-dim obs
     
     print("Starting training...")
     
@@ -277,7 +280,15 @@ def train(args):
     
     for i_episode in range(args.max_episodes):
         # --- 1. Collect Rollouts (Interaction) ---
-        memory = {'states': [], 'actions': [], 'logprobs': [], 'rewards': [], 'next_states': [], 'dones': []}
+        memory = {
+            'states': [],
+            'actions': [],
+            'pre_tanh_actions': [],
+            'logprobs': [],
+            'rewards': [],
+            'next_states': [],
+            'dones': []
+        }
         
         # Prepare seed
         available_scenarios = env.config["num_scenarios"]
@@ -338,7 +349,7 @@ def train(args):
             import gc
             gc.collect()
             
-            env = MAGAILScenarioEnv(config=env_config, agent2policy={})
+            env = BCScenarioEnv(env_config, agent2policy={})
             obs_dict = env.reset(seed=seed)
         
         episode_reward = 0
@@ -349,6 +360,7 @@ def train(args):
             # Select actions for all agents
             actions = {}
             action_logprobs = {}
+            pre_tanh_actions = {}
             
             # obs_dict: {agent_id: obs}
             # MultiAgentScenarioEnv usually returns a dict {agent_id: obs}
@@ -386,9 +398,10 @@ def train(args):
                 obs_dict = new_obs_dict
 
             for agent_id, obs in obs_dict.items():
-                act, logprob = ppo_agent.select_action(obs) # Select action returns numpy
+                act, logprob, pre_tanh = ppo_agent.select_action(obs) # Select action returns numpy
                 actions[agent_id] = act.flatten() # (2,)
                 action_logprobs[agent_id] = logprob # scalar
+                pre_tanh_actions[agent_id] = pre_tanh.flatten()
                 
             # Step Env
             next_obs_dict, rewards, dones, infos = env.step(actions)
@@ -398,6 +411,7 @@ def train(args):
                 if agent_id in actions:
                     memory['states'].append(obs)
                     memory['actions'].append(actions[agent_id])
+                    memory['pre_tanh_actions'].append(pre_tanh_actions[agent_id])
                     memory['logprobs'].append(action_logprobs[agent_id])
                     
                     # Store standard environmental reward for logging (not used for update in GAIL)
@@ -407,7 +421,7 @@ def train(args):
                     # Next state
                     if agent_id in next_obs_dict:
                         memory['next_states'].append(next_obs_dict[agent_id])
-                        memory['dones'].append(False)
+                        memory['dones'].append(dones.get("__all__", False))
                     else:
                         # Agent finished/vanished
                         # We need a dummy next state or handle done correctly
@@ -460,6 +474,10 @@ def train(args):
             disc_loss = exp_loss + pol_loss
             disc_loss.backward()
             disc_optimizer.step()
+
+            with torch.no_grad():
+                disc_acc_exp = (exp_preds > 0.5).float().mean().item()
+                disc_acc_pol = (pol_preds < 0.5).float().mean().item()
             
             # --- 3. Update Policy with GAIL Rewards ---
             # Reward = -log(1 - D(s, a))
@@ -494,11 +512,18 @@ def train(args):
             writer.add_scalar('Loss/Discriminator', disc_loss.item(), i_episode)
             writer.add_scalar('Loss/Policy', ppo_loss, i_episode)
             writer.add_scalar('Reward/Mean_GAIL', np.mean(all_gail_rewards), i_episode)
+            if batch_size > 0:
+                writer.add_scalar('Acc/Disc_Expert', disc_acc_exp, i_episode)
+                writer.add_scalar('Acc/Disc_Policy', disc_acc_pol, i_episode)
+            if len(memory['actions']) > 0:
+                action_arr = np.array(memory['actions'])
+                action_clip_ratio = (np.abs(action_arr) > 0.98).mean()
+                writer.add_scalar('Policy/ActionClipRatio', action_clip_ratio, i_episode)
         
         print(f"Episode {i_episode}: Disc Loss {disc_loss.item():.4f} | PPO Loss {ppo_loss:.4f} | Mean Reward {np.mean(all_gail_rewards):.4f}")
         
         if i_episode % 50 == 0:
-            ppo_agent.save(os.path.join(args.log_dir, f"model_{i_episode}"))
+            ppo_agent.save(os.path.join(args.save_dir, f"model_{i_episode}"))
 
     env.close()
     if writer:
@@ -511,11 +536,13 @@ if __name__ == '__main__':
     parser.add_argument("--batch_size", type=int, default=1024)
     parser.add_argument("--max_episodes", type=int, default=1000)
     parser.add_argument("--num_scenarios", type=int, default=100)
-    parser.add_argument("--log_dir", type=str, default="runs/magail_exp")
+    parser.add_argument("--log_dir", type=str, default="logs/magail", help="TensorBoard log directory")
+    parser.add_argument("--save_dir", type=str, default="models/magail", help="Directory to save model checkpoints")
     
     args = parser.parse_args()
     
-    # Create log dir
+    # Create log dir and save dir
     os.makedirs(args.log_dir, exist_ok=True)
+    os.makedirs(args.save_dir, exist_ok=True)
     
     train(args)