更新 .gitignore 和训练脚本，添加可视化脚本

scripts/README_visualize.md

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+# 模型可视化脚本使用说明
+
+## 功能
+使用训练好的MAGAIL模型在环境中运行，并生成俯瞰效果图（top-down view）。
+
+## 使用方法
+
+### 基本用法
+
+```bash
+python scripts/visualize_trained_model.py \
+    --model_dir runs/magail_0113 \
+    --episode 1250 \
+    --data_dir data/exp_filtered \
+    --num_scenarios 1 \
+    --output_dir visualizations
+```
+
+### 参数说明
+
+- `--model_dir`: 模型保存目录（例如：`runs/magail_0113`）
+- `--episode`: 要加载的episode编号（例如：`1250`）
+- `--data_dir`: Waymo数据目录（默认：`data/exp_filtered`）
+- `--start_index`: 起始场景索引（默认：`0`）
+- `--num_scenarios`: 要运行的场景数量（默认：`1`）
+- `--horizon`: 每个episode的最大步数（默认：`200`）
+- `--output_dir`: 输出图像保存目录（默认：`visualizations`）
+- `--save_all_frames`: 保存所有帧（否则按间隔保存）
+- `--save_interval`: 保存帧的间隔，当不使用`--save_all_frames`时生效（默认：`10`）
+- `--gif_duration`: GIF每帧持续时间（毫秒），默认50ms（20fps）。值越小，GIF播放越快
+
+### 示例
+
+#### 1. 查看最新训练的模型（episode 1250）
+```bash
+python scripts/visualize_trained_model.py \
+    --model_dir runs/magail_0113 \
+    --episode 1250 \
+    --num_scenarios 3 \
+    --output_dir visualizations/episode_1250
+```
+
+#### 2. 保存所有帧（用于制作视频）
+```bash
+python scripts/visualize_trained_model.py \
+    --model_dir runs/magail_0113 \
+    --episode 1250 \
+    --save_all_frames \
+    --output_dir visualizations/episode_1250_all_frames
+```
+
+#### 3. 每5步保存一帧
+```bash
+python scripts/visualize_trained_model.py \
+    --model_dir runs/magail_0113 \
+    --episode 1250 \
+    --save_interval 5 \
+    --output_dir visualizations/episode_1250_sparse
+```
+
+#### 4. 生成更快的GIF（30fps）
+```bash
+python scripts/visualize_trained_model.py \
+    --model_dir runs/magail_0113 \
+    --episode 1250 \
+    --gif_duration 33 \
+    --output_dir visualizations/episode_1250
+```
+
+## 输出
+
+脚本会在指定的输出目录中创建以下文件：
+- `scenario_{idx}.gif`: **场景动画GIF**（主要输出）
+- `scenario_{idx}_step_{step:04d}.png`: 每个保存步骤的俯瞰图（可选）
+- `scenario_{idx}_final.png`: 每个场景的最终状态图
+
+### GIF格式
+- 分辨率：1600x900
+- 格式：GIF动画
+- 包含完整的场景运行过程
+- 显示场景编号、步数、智能体数量和奖励信息
+- 默认帧率：20fps（可通过`--gif_duration`调整）
+
+### 图像格式
+- 分辨率：1600x900
+- 格式：PNG
+- 包含语义地图和车辆轨迹
+
+## 注意事项
+
+1. **GPU要求**: 脚本需要CUDA支持，如果没有GPU会自动使用CPU（速度较慢）
+2. **渲染模式**: 使用MetaDrive的top-down渲染模式，会弹出窗口显示实时渲染
+3. **内存占用**: 如果保存所有帧，会占用较多磁盘空间
+4. **场景数据**: 确保`--data_dir`指向正确的Waymo数据目录
+
+## 故障排除
+
+### 模型文件不存在
+```
+FileNotFoundError: 模型文件不存在: runs/magail_0113/model_1250_actor.pth
+```
+**解决**: 检查模型目录和episode编号是否正确
+
+### 场景数据不存在
+```
+ValueError: Data directory not found
+```
+**解决**: 确保`--data_dir`指向正确的数据目录
+
+### 渲染失败
+如果遇到渲染相关错误，可以尝试：
+- 降低`film_size`参数（在脚本中修改）
+- 使用无头模式（需要修改脚本）

scripts/launch_tensorboard.py

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+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_trained_policy.py

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+import argparse
+import os
+import sys
+import torch
+import numpy as np
+import time
+
+# Add project root to Python path
+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 train_magail import Actor, MAGAILScenarioEnv
+from metadrive.engine.engine_utils import close_engine
+
+def visualize_model(args):
+    # 1. Load Environment
+    data_path = os.path.abspath(args.data_dir)
+    env_config = {
+        "data_directory": data_path,
+        "is_multi_agent": True,
+        "num_controlled_agents": 3,
+        "horizon": args.horizon,
+        "use_render": True,  # Visualisation enabled
+        "sequential_seed": True,
+        "start_scenario_index": args.start_index,
+        "num_scenarios": args.num_scenarios,
+        "log_level": 40,
+    }
+    
+    print("Initializing MAGAILScenarioEnv...")
+    try:
+        env = MAGAILScenarioEnv(config=env_config, agent2policy={})
+    except Exception as e:
+        print(f"Error init env: {e}. Trying to close lingering engine...")
+        try:
+            close_engine()
+        except:
+            pass
+        env = MAGAILScenarioEnv(config=env_config, agent2policy={})
+
+    # 2. Load Model
+    state_dim = 45
+    action_dim = 2
+    
+    actor = Actor(state_dim, action_dim).cuda()
+    
+    model_path = args.model_path
+    if not os.path.exists(model_path):
+        # Try to find it in runs/
+        potential_path = os.path.join("runs", "magail_production", model_path)
+        if os.path.exists(potential_path):
+            model_path = potential_path
+        else:
+            # Try appending _actor.pth
+            potential_path = model_path + "_actor.pth"
+            if os.path.exists(potential_path):
+                model_path = potential_path
+            else:
+                raise ValueError(f"Model path {args.model_path} not found.")
+                
+    print(f"Loading model from {model_path}...")
+    actor.load_state_dict(torch.load(model_path))
+    actor.eval()
+    
+    # 3. Run Loop
+    try:
+        for i in range(args.start_index, args.start_index + args.num_scenarios):
+            print(f"\n--- Playing Scenario {i} ---")
+            
+            # Reset
+            try:
+                # Use sequential seed logic or specific seed?
+                # ExpertReplayEnv/ScenarioEnv logic: seed matches scenario index if configured right
+                obs_dict = env.reset(seed=i)
+            except Exception as e:
+                print(f"Error resetting {i}: {e}. Skipping.")
+                # Try soft reset
+                try:
+                    close_engine()
+                    env = MAGAILScenarioEnv(config=env_config, agent2policy={})
+                except:
+                    pass
+                continue
+                
+            print(f"Scenario loaded. Controlled agents: {len(obs_dict)}")
+            
+            step_count = 0
+            while True:
+                actions = {}
+                # Inference
+                for agent_id, obs in obs_dict.items():
+                    # Preprocess obs: (45,) -> (1, 45) tensor
+                    obs_tensor = torch.FloatTensor(obs).unsqueeze(0).cuda()
+                    with torch.no_grad():
+                        dist = actor(obs_tensor)
+                        # Deterministic action for viz? Or sample?
+                        # Usually deterministic (mean) is better for checking performance
+                        # But training uses sample.
+                        if args.deterministic:
+                            action = torch.tanh(dist.mean) # Use mean of Gaussian
+                        else:
+                            pre_tanh = dist.sample()
+                            action = torch.tanh(pre_tanh)
+                            
+                    actions[agent_id] = action.cpu().numpy().flatten()
+                
+                # Step
+                obs_dict, rewards, dones, infos = env.step(actions)
+                
+                # Render
+                env.render(
+                    mode="top_down",
+                    text={
+                        "Scenario": i,
+                        "Step": step_count,
+                        "Agents": len(obs_dict)
+                    }
+                )
+                
+                step_count += 1
+                # time.sleep(0.02) # Slow down if needed
+                
+                if dones["__all__"] or step_count >= args.horizon:
+                    print(f"Scenario finished at step {step_count}")
+                    break
+                    
+    except KeyboardInterrupt:
+        print("Interrupted.")
+    finally:
+        env.close()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", type=str, required=True, help="Path to actor model pth (e.g. runs/magail_production/model_50_actor.pth)")
+    parser.add_argument("--data_dir", type=str, default="data/exp_filtered")
+    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=200)
+    parser.add_argument("--deterministic", action="store_true", help="Use mean action instead of sampling")
+    
+    args = parser.parse_args()
+    visualize_model(args)