# DynamisLab — 训练目录 > `src/drl_pinball/train/` > 更新: 2026-07-12 --- ## 命名规则 ``` {domain}_{variant}_{method}_seed{seed} domain: kar = Karman | ill = Illusion variant: re{60,100,200,400} = 雷诺数 | d{075,15,2} = 扰动圆柱直径(L) | {1L,15L,075L,2L} = 目标圆柱尺寸 method: sc = from scratch | tr = transfer from re100 baseline seed: seed{41..45} Examples: kar_re100_sc_seed45 — Karman Re100 scratch, seed 45 kar_d075_sc_seed44 — Karman 0.75L dist-cyl scratch kar_d15_tr_seed45 — Karman 1.5L dist-cyl transfer kar_re200_sc_seed43 — Karman Re200 scratch ill_2L_sc_seed43 — Illusion 2L target scratch ``` --- ## 目录结构 ``` train/ ├── train_karman.py # Karman PPO 训练 (核心) ├── train_illusion.py # Illusion PPO 训练 ├── calibrate.py # Phase 0 校准 │ ├── env_karman.py # Karman 环境 ├── env_illusion.py # Illusion 环境 ├── symmetry_wrapper.py # G-symmetry 数据增强 │ ├── scripts/ # 启动脚本 │ ├── train_baseline.sh # kar_re100_sc multi-seed │ ├── train_illusion.sh # ill_*_sc 4 个尺寸 │ ├── vardist_scratch.sh # kar_d*_sc (推荐) │ ├── vardist_transfer.sh # kar_d*_tr (参考) │ ├── crossre_scratch.sh # kar_re*_sc │ ├── crossre_transfer.sh # kar_re*_tr (参考) │ └── resume.sh # 中断恢复 │ ├── calibrations/ # 校准文件 │ ├── kar_re100/ # Re100 基准 │ ├── kar_d*_sc/ # 变直径 scratch (generic SIM_BP) │ ├── kar_d*_tr/ # 变直径 transfer (实测 SIM_BP) │ ├── kar_re*_sc/ # 变雷诺数 scratch (generic SIM_BP) │ ├── kar_re*/ # 变雷诺数 transfer (实测 SIM_BP) │ └── ill_*/ # Illusion │ ├── output/ # 训练输出 (21 dirs) │ ├── kar_re100_sc_seed{41..45}/ │ ├── kar_d*_sc_seed{44,45}/ │ ├── kar_d*_tr_seed{44,45}/ │ ├── kar_re*_sc_seed43/ │ ├── kar_re*_tr_seed43/ │ └── ill_*_sc_seed43/ │ ├── archive/ # 旧版本 (保留) ├── README.md # 当前文件 ├── VARDIST_ANALYSIS.md ├── CROSSRE_ANALYSIS.md └── SERVER_DEPLOY.md ``` --- ## 快速开始 ### 基准训练 ```bash cd scripts bash train_baseline.sh --gpu 0 --episodes 500 # → output/kar_re100_sc_seed{41..45}/ ``` ### 变直径 scratch (推荐) ```bash cd scripts bash vardist_scratch.sh --gpu 0 bash vardist_scratch.sh --only d075 # → output/kar_d075_sc_seed44/ etc. ``` ### 变雷诺数 scratch ```bash cd scripts bash crossre_scratch.sh --gpu 0 bash crossre_scratch.sh --only re200 # → output/kar_re200_sc_seed43/ etc. ``` ### Illusion ```bash cd scripts bash train_illusion.sh --gpu 0 # → output/ill_2L_sc_seed43/ etc. ``` ### 中断恢复 ```bash cd scripts bash resume.sh --case kar_re60_sc --seed 43 --resume 460 --episodes 500 ``` --- ## 核心训练脚本 ### `train_karman.py` ```bash conda run -n pycuda_3_10 python -u train_karman.py \ --case-name kar_re100_sc --device-id 0 --seed 42 \ --config config.json --calibration calibrations/kar_re100/calibration.json \ --total-episodes 500 [--transfer-model PATH] [--resume-from N] ``` | 参数 | 默认值 | 说明 | |------|:-----:|------| | n_steps | 2048 | PPO 每轮收集步数 | | batch_size | 64 | minibatch | | n_epochs | 10 | 经验重用次数 | | lr | 3e-4 | 学习率 | | gamma | 0.995 | 折扣因子 | | net_arch | [64, 64] | MLP 两层 sin 激活 | --- ## 最终结果 ### Re100 基准 — `kar_re100_sc` | Seed | r_sim | r_cd | r_cl | |:----:|:-----:|:----:|:----:| | 45 | 0.883 | 0.977 | 0.982 | | Avg | 0.866 | 0.971 | 0.965 | ### Vardist 变直径 | Case | 方式 | r_sim | r_cd | r_cl | |------|:----:|:-----:|:-----:|:-----:| | **kar_d075_sc** | scratch | **0.918** | 0.954 | 0.879 | | kar_d075_tr | transfer | 0.450 | 0.585 | 0.236 | | **kar_d15_sc** | scratch | **0.904** | 0.991 | 0.992 | | kar_d15_tr | transfer | 0.791 | 0.918 | 0.470 | | **kar_d2_sc** | scratch | **0.793** | 0.967 | 0.961 | | kar_d2_tr | transfer | 0.673 | 0.537 | 0.192 | > Scratch 全面领先 transfer。 ### Cross-Re 变雷诺数 | Case | 方式 | r_sim | r_cd | r_cl | |------|:----:|:-----:|:-----:|:-----:| | kar_re60_tr | transfer | 0.446 | **0.882** | 0.936 | | kar_re60_sc | scratch | 0.261 | **0.987** | 0.981 | | **kar_re200_sc** | scratch | **0.664** | 0.667 | 0.281 | | kar_re200_tr | transfer | 0.374 | 0.749 | 0.427 | | **kar_re400_sc** | scratch | **0.509** | 0.714 | 0.420 | | kar_re400_tr | transfer | 0.293 | 0.883 | 0.571 | > Scratch sim 最优，transfer CD 最优。两者均保留。 ### Illusion | Case | r_sim | r_cd | r_cl | |------|:-----:|:-----:|:-----:| | ill_1L_sc | 0.725 | 0.774 | 0.449 | | ill_15L_sc | 0.810 | 0.825 | 0.384 | | ill_075L_sc | 0.794 | 0.840 | 0.486 | | **ill_2L_sc** | **0.896** | 0.863 | 0.570 | --- ## Calibration 参考 | Calibration | 物理 | SI | SIM_BP | K_CD/K_CL | 对应 output | |------------|------|:--:|--------|:---------:|------------| | `kar_re100` | Re=100, 1.0L | 800 | 实测 | 50/100 | `kar_re100_sc` | | `kar_d075_sc` | Re=100, 0.75L | 800 | generic | 50/100 | `kar_d075_sc` | | `kar_d075_tr` | Re=100, 0.75L | 800 | 实测 | 50/100 | `kar_d075_tr` | | `kar_d15_sc` | Re=100, 1.5L | 800 | generic | 50/100 | `kar_d15_sc` | | `kar_d15_tr` | Re=100, 1.5L | 800 | 实测 | 50/100 | `kar_d15_tr` | | `kar_d2_sc` | Re=100, 2.0L | 800 | generic | 50/100 | `kar_d2_sc` | | `kar_d2_tr` | Re=100, 2.0L | 800 | 实测 | 50/100 | `kar_d2_tr` | | `kar_re60` | Re=60 | 800 | 实测 | 12/25 | `kar_re60_tr` | | `kar_re60_sc` | Re=60 | 800 | generic | 50/100 | `kar_re60_sc` | | `kar_re200` | Re=200 | 500 | 实测 | 12/25 | `kar_re200_tr` | | `kar_re200_sc` | Re=200 | 500 | generic | 50/100 | `kar_re200_sc` | | `kar_re400` | Re=400 | 400 | 实测 | 12/25 | `kar_re400_tr` | | `kar_re400_sc` | Re=400 | 400 | generic | 50/100 | `kar_re400_sc` | | `ill_*` | Illusion | — | 实测 | 12/25 | `ill_*_sc` | --- ## Key Findings 1. **SIM_BP 映射是跨场景训练的最关键变量。** 实测 gap ≠ 0.35 会导致 reward 梯度异常。 - Generic SIM_BP `[0, 0.30, 0.65, 0.79, 0.89, 1.0]` (gap=0.35) → 稳定学习 2. **变直径: scratch > transfer.** Generic SIM_BP + K_CD/CL=50/100 + lr=3e-4 + 500ep 是最优配方。 3. **变雷诺数: scratch 和 transfer 各有侧重。** 两者均保留。 4. **变雷诺数存在 CD/CL ↔ r_sim 根本性 trade-off。** 训练越久 r_cd 越高但 r_sim 越低（re60_sc: Ep60 r_sim=0.48 → Ep390 r_sim=0.26, r_cd=0.99）。CD/CL reward 分量主导了优化方向。 5. **降低 W_drag/W_lift (0.1) 有害。** CD/CL reward 是稳定器而非瓶颈。 6. **Ep1 r_sim ≥ 0.5 可预测 transfer 成功。**