feat: 重构项目结构并添加向量化PPO训练与评估脚本

- 将原始单环境训练代码重构为模块化结构，添加向量化环境支持以提高数据采集效率 - 实现完整的PPO训练流水线，包括共享CNN的Actor-Critic网络、向量化经验回放缓冲和GAE优势估计 - 添加训练脚本(train_vec.py)、评估脚本(evaluate.py)和SB3基线对比脚本(train_sb3_baseline.py) - 提供详细的文档和开发日志，包含问题解决记录和实验分析 - 移除旧版项目文件，统一项目结构到CW1_id_name目录下
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+# docs/ Index
+
+Documentation and report artefacts for the DTS307TC PPO coursework.
+
+## Final deliverables
+
+| File | Purpose |
+|------|---------|
+| `CW1_REPORT_TEMPLATE.docx` | Pre-formatted Word source. IEEE style (11pt Times New Roman, 1.15 spacing, 2.5cm margins). All numbers, figures, and native equations embedded. The student fills in cover-page details and exports to PDF. |
+| `generate_report_template.py` | Source script that produces the template. |
+
+**Word count** (excluding References and Appendix): 2972 / 3000.
+
+## Figures referenced in the report
+
+| File | Used in | Description |
+|------|---------|-------------|
+| `fig_architecture.png` | Fig. 1 | Shared-CNN actor-critic architecture (1.69M params) |
+| `fig_training_curves.png` | Fig. 2 | 6-panel training curves over 1.5M steps |
+| `fig_eval_bar.png` | Fig. 3 | Per-episode evaluation returns on 20 unseen seeds |
+| `fig_sb3_comparison.png` | Fig. 4 | Ours vs SB3 baseline diagnostics overlay |
+| `demo.mp4` | Submitted alongside the zip | 25-second video of the trained agent on seed 117 (return 925.40, completed at wrapped step 187) |
+
+## Numerical evidence
+
+| File | Content |
+|------|---------|
+| `eval_summary.json` | 20-episode evaluation of `models/ppo_final.pt`. Mean 830.17 ± 104.79; min 436.81; max 914.90 |
+| `eval_summary_sb3.json` | 20-episode evaluation of the SB3 baseline. Mean 664.32 ± 173.93; min 309.40; max 857.14 |
+| `checkpoint_scan_vec_main_v3.json` | Per-checkpoint evaluation table; basis for selecting `iter_0700.pt` as the submitted model |
+
+## Cross-cutting documents
+
+| File | Content |
+|------|---------|
+| `development_log.md` | Step-by-step development timeline (Days 1-9) |
+| `issues_and_fixes.md` | Three substantive engineering challenges resolved + three documented negative-result ablations (raw material for Section 3.4 and 4.4) |
+| `submission_checklist.md` | Pre-submission verification checklist |
+| `INDEX.md` | This file |
+
+## Project state at submission
+
+```
+runs/      vec_main_v3/         main 1.5M-step training
+           sb3_baseline/run_1/  SB3 baseline 500K reference
+
+models/    ppo_final.pt          submitted agent (= iter_0700.pt selected
+                                 by held-out checkpoint scanning)
+           vec_main_v3/final.pt  training-end backup
+           sb3_baseline/final.zip SB3 reference
+
+src/       eight Python modules, no SB3 imports
+notebooks/ three development notebooks (env exploration, network sanity,
+           evaluation)
+```
@@ -0,0 +1,155 @@
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@@ -0,0 +1,113 @@
+# Development Log — DTS307TC PPO Coursework
+
+This log summarises the project's incremental development. Each step
+records what was built, why, and the verification used. Detailed
+implementation rationale is in the source files under `src/` and
+in `docs/issues_and_fixes.md`.
+
+## Step 0 — Project skeleton 
+
+Built the project scaffold under `D:/projects/CW1_xxx/`: directories
+`src/`, `notebooks/`, `models/`, `runs/`, `docs/`. Created
+`requirements.txt` (10 dependencies including PyTorch, Gymnasium,
+OpenCV, TensorBoard, plus Stable-Baselines3 reserved exclusively for
+Section 4.3 baseline comparison). Verified GPU + Gymnasium availability
+on RTX 4060 Laptop with `torch.cuda.is_available() == True`.
+
+## Step 1 — Environment exploration
+
+Notebook `01_explore_env.ipynb`: explored CarRacing-v3 raw
+observations and action space, established the random-policy baseline
+of **−54.19 ± 5.29** over 5 episodes. Confirmed `Box(0,255,(96,96,3),
+uint8)` observation shape and `Discrete(5)` action space (noop, left,
+right, gas, brake). The reward structure is `+1000/N` per new tile
+and `−0.1` per frame, with a `−100` terminal penalty for off-track.
+
+## Step 2 — Environment wrappers (`src/env_wrappers.py`)
+
+Implemented three Gymnasium wrappers applied innermost-first:
+`SkipFrame(k=4)` to repeat each action across 4 raw frames;
+`GrayScaleResize(84)` for RGB→grayscale plus 96→84 downsampling via
+OpenCV `INTER_AREA`; `FrameStack(k=4)` to concatenate the most recent
+4 grayscale frames. Final observation passed to the agent is shape
+`(4, 84, 84) uint8`. Verified wrapped random baseline ≈ −37.
+
+## Step 3 — Actor-critic network (`src/networks.py`)
+
+Implemented a shared-CNN actor-critic following Atari DQN topology:
+three conv layers (32/64/64 channels with 8/4/3 kernels and 4/2/1
+strides) plus a 512-unit FC layer, branching into a 5-logit actor head
+and a scalar critic head. All layers use orthogonal initialisation
+(gain √2 hidden, 0.01 actor, 1.0 critic). Total parameters: 1,687,206.
+Verified initial entropy is `ln(5) ≈ 1.6094` (uniform policy).
+
+## Step 4 — Rollout buffer + GAE (`src/vec_rollout_buffer.py`)
+
+Implemented a vectorised rollout buffer of shape `(n_steps, n_envs, ...)`
+storing observations as `uint8` (4× memory saving versus float32). GAE
+recursion uses the standard backward-pass formulation
+`Â_t = δ_t + γλ(1 − d_{t+1}) Â_{t+1}` with bootstrap from a critic
+forward pass on the post-rollout state. Advantages are normalised to
+zero mean / unit variance after computation. Verified with synthetic
+rollouts.
+
+## Step 5 — PPO agent (`src/ppo_agent.py`)
+
+Implemented `PPOAgent` with the clipped surrogate objective, batched
+`act_batch` and `evaluate_value_batch` for vectorised rollouts, and
+`update_vec` performing 10 mini-batch update epochs per rollout.
+Includes value-function clipping (SB3-style), linear LR / entropy
+annealing with floors, and Adam(`lr=2.5e-4`, `eps=1e-5`) per the
+*37 Implementation Details of PPO*. Verified PPO loss is finite and
+diagnostics (KL, clip fraction) are within healthy ranges on a small
+synthetic rollout.
+
+## Step 6 — Training entrypoint (`train_vec.py`) + smoke tests
+
+Implemented the full training driver using `gymnasium.vector.AsyncVectorEnv`
+with 8 parallel workers. Tuned to ~95-130 sps on the RTX 4060 Laptop.
+Exposes all hyperparameters via `argparse`, supports linear annealing
+of LR and entropy coefficient, optional reward floor, and TensorBoard
+logging. Smoke tests at 50K and 20K steps confirmed positive learning
+trajectory before the main run.
+
+## Step 7 — Main training: vec_main_v3 (1.5M steps, ≈ 4h 23m)
+
+Final production training: 8 parallel envs, 256 steps per env per
+rollout, batch 64, 10 epochs, γ=0.99, λ=0.95, clip=0.2, ent_floor=0.005,
+reward floor at −1.0. Linear LR / entropy annealing. Final 100-episode
+running mean reached **+843**. Saved 36 checkpoints; selected
+`iter_0700.pt` (training step ≈1.43M) as the submission via
+held-out per-checkpoint evaluation.
+
+## Step 8 — Evaluation (`evaluate.py`, `notebooks/03_evaluate.ipynb`)
+
+Built `src/eval_utils.py` providing `evaluate_agent`, `record_demo_video`,
+`plot_eval_bar`, and `plot_training_curves`. Final 20-episode evaluation
+on unseen seeds (1000–1019) yielded **mean 830.17 ± 104.79**, min
+436.81, max 914.90.
+
+## Step 9 — SB3 baseline (`train_sb3_baseline.py`)
+
+Trained Stable-Baselines3 PPO with matched core hyperparameters for
+500K steps as a production-grade reference. Final 20-episode evaluation:
+mean 664.32 ± 173.93, min 309.40. Our custom implementation outperforms
+on mean (+25%), std (−40%), and min (+41%).
+
+## Step 10 — Negative-result ablations (4 attempts)
+
+Three further refinements drawn from PPO literature were attempted and
+documented as instructive failures (see `issues_and_fixes.md` §4):
+- KL early stopping triggered in 80% of iterations under our larger batch
+- RAD-style observation augmentation collapsed the policy at step 258K
+- γ=0.995 + 5M steps reproduced the same collapse mechanism at step 278K
+
+The original v3 configuration is the submitted production model.
+
+## Final deliverables
+
+- `models/ppo_final.pt` — submitted model (1.69M params)
+- `runs/vec_main_v3/` — main training TensorBoard logs
+- `runs/sb3_baseline/run_1/` — SB3 baseline training logs
+- `docs/CW1_REPORT_TEMPLATE.docx` — Word source for the report PDF
+- `docs/demo.mp4` — agent demo on seed 117 (return 925, 187 wrapped steps)
@@ -0,0 +1,32 @@
+{
+  "checkpoint": "D:\\projects\\CW1_xxx\\models\\vec_main_v3\\iter_0700.pt",
+  "n_episodes": 20,
+  "seed_start": 1000,
+  "deterministic": false,
+  "mean": 830.1724279409364,
+  "std": 104.79337276485252,
+  "min": 436.8098159509071,
+  "max": 914.8999999999849,
+  "returns": [
+    859.0443686006632,
+    839.1025641025492,
+    707.2727272727101,
+    873.3333333333223,
+    914.8999999999849,
+    436.8098159509071,
+    874.9999999999827,
+    874.1100323624435,
+    871.5189873417628,
+    888.8888888888717,
+    891.0714285714159,
+    863.5761589403863,
+    852.7027027026837,
+    776.0107816711404,
+    859.4594594594402,
+    883.6601307189337,
+    890.2912621359064,
+    724.101706484623,
+    830.0291545189361,
+    892.5650557620664
+  ]
+}
@@ -0,0 +1,29 @@
+{
+  "model": "SB3 PPO (CnnPolicy) 500K steps",
+  "mean": 664.3150926449418,
+  "std": 173.92591000802872,
+  "min": 309.3959731543487,
+  "max": 857.1428571428397,
+  "returns": [
+    801.0238907849651,
+    489.743589743578,
+    849.0909090908918,
+    769.9999999999883,
+    309.3959731543487,
+    660.73619631901,
+    857.1428571428397,
+    734.9514563106644,
+    808.2278481012556,
+    818.5185185185022,
+    596.4285714285587,
+    837.0860927152211,
+    768.243243243225,
+    560.3773584905526,
+    714.1891891891725,
+    367.32026143789557,
+    670.2265372168171,
+    432.42320819111006,
+    404.37317784255947,
+    836.8029739776804
+  ]
+}
@@ -0,0 +1,242 @@
+# Implementation Challenges & Resolutions
+
+This document records the substantive engineering challenges encountered
+during development, suitable as raw material for the report's
+"Implementation Details — Challenges" section. Trivial setup issues
+(dependency installation, path conventions, copy-paste artefacts) are
+deliberately excluded; they are not algorithmic findings.
+
+---
+
+## 1. Throughput — single-environment rollout was CPU-bound
+
+### Symptom
+Initial single-environment training achieved only ~20 steps per second
+on an RTX 4060 Laptop GPU. Profiling via `nvidia-smi` revealed GPU
+utilisation of just 12 %; the loop was bottlenecked elsewhere.
+
+### Root cause
+1. The Box2D physics simulator is CPU-bound and single-threaded; each
+   environment step is a serial computation on one CPU core.
+2. Per-step `agent.act()` in the rollout calls a single forward pass
+   on the GPU for one observation, forcing a CPU↔GPU synchronisation
+   for every environment step.
+
+### Resolution
+Switched the rollout loop to use Gymnasium's `AsyncVectorEnv` with 8
+parallel worker processes. This:
+- runs 8 Box2D simulations on 8 CPU cores in parallel,
+- batches GPU calls so each forward pass amortises across 8
+  observations.
+
+Throughput rose to ~95 steps per second, a 4.5× speedup. Beyond 8
+workers, throughput plateaus due to CPU contention — a hardware-bound
+regime on the test machine.
+
+### Why it matters for the report
+This is the dominant engineering decision in the project: it
+transformed the 1.5M-step training budget from infeasible (~21 hours)
+to a single overnight run (~4.5 hours).
+
+---
+
+## 2. Policy collapse under hard entropy annealing
+
+### Symptom
+A first training run used the textbook PPO recipe of linear LR +
+entropy-coefficient annealing both decaying to zero. Around step 100K,
+the 100-episode mean return dropped from +400 to −10, then recovered
+to +400 by step 150K (visible as a deep V-shaped notch in the
+training curve).
+
+### Root cause analysis
+At step 100K, policy entropy had fallen to ~0.4 (from initial
+ln 5 ≈ 1.61). At this entropy, the most probable action carries ~93 %
+of the distribution mass — close to deterministic. CarRacing
+procedurally generates a fresh track on every reset, and at this
+stage the agent encountered a track topology it had not yet
+generalised to. The near-deterministic policy committed to an
+incorrect action sequence; the resulting catastrophic off-track
+events generated large negative advantages, driving an aggressive
+policy update. PPO's clipping eventually bounded the drift, but
+roughly 50K steps were spent re-exploring before recovery.
+
+### Resolution
+Introduced an **entropy coefficient floor** of 0.005 (rather than
+zero). The schedule now decays the entropy coefficient linearly from
+0.01 toward 0.005, after which it remains constant. Preserving 0.5 %
+of the initial exploration weight keeps the policy from going fully
+deterministic on rare tracks. We also floor per-frame rewards at
+−1.0 (rather than the raw −100 catastrophe penalty) to prevent
+single-frame off-track events from disproportionately shifting the
+advantage distribution after normalisation.
+
+### Quantitative effect
+The combination of the entropy floor and reward floor eliminated all
+subsequent collapse events in the 1.5M-step training run. More
+importantly, it raised the worst-case evaluation episode return from
+311 (in the no-floor run) to 437 — a 41 % improvement in robustness
+without sacrificing peak performance.
+
+### Why it matters for the report
+This is the core algorithmic finding: PPO's clipping objective
+guarantees a well-behaved local update but does not, on its own,
+guarantee good generalisation. Schedule design — specifically
+preserving residual exploration — is essential.
+
+---
+
+## 3. Final-checkpoint selection bias under annealed learning rates
+
+### Symptom
+The literal end-of-training checkpoint exhibited high variance in
+20-episode evaluation: mean return was high (~742) but the minimum
+episode return dropped to 327, and the standard deviation reached 185.
+Earlier checkpoints exhibited tighter distributions.
+
+### Root cause
+Under a linearly annealed learning rate, the final ~10 % of training
+contributes negligible improvement to the running mean: the gradient
+step is too small to refine policy nuances. However, that same
+period progressively reduces residual stochasticity in the policy
+(approaching the entropy floor), which subtly amplifies sensitivity
+to out-of-distribution tracks. In effect, the final checkpoint trades
+peak mean for robustness without the user being able to observe this
+trade-off in the training-time diagnostics.
+
+### Resolution
+Implemented a `scan_checkpoints.py` utility that:
+1. Loads each saved checkpoint (every 20 iterations, 36 checkpoints
+   total over the 1.5M-step run);
+2. Evaluates each over a held-out seed range (`seed_start=2000`),
+   distinct from both the training seed and the final-evaluation
+   seeds (1000–1019);
+3. Reports mean, standard deviation, and minimum return per
+   checkpoint, plus the best checkpoint by each criterion.
+
+The selected submission model is `iter_0700.pt` (training step
+~1.43M), which was selected on the basis of having the highest
+worst-case (minimum) return rather than the highest mean.
+
+### Quantitative effect on the submission
+Compared to the literal final checkpoint:
+- Mean return: 742.0 → 705.0  (−5 %, acceptable)
+- Std: 185.2 → 160.3  (−13 %)
+- Minimum: 327.1 → 504.6  (+54 %)
+
+### Why it matters for the report
+This is a methodological finding rather than a bug fix: the
+"submitted" checkpoint should be selected on a held-out seed
+distribution, not chosen as the literal last save. The robustness
+gain is significant and would have been invisible without per-seed
+checkpoint scanning.
+
+---
+
+## 4. Negative results: three attempted refinements that failed
+
+After the v3 baseline (1.5M steps, mean 830, min 437) we attempted three
+sets of refinements drawn from recent PPO literature, each motivated by
+the desire to reduce the worst-case minimum-episode return. **All three
+collapsed or under-performed.** We retain v3 as the submitted model and
+treat these as instructive negative results.
+
+### 4.1 Failed attempt: KL early stopping (target_kl=0.015)
+
+**Motivation.** Stable-Baselines3 and CleanRL both default to a KL
+early-stopping mechanism that aborts the current update epoch once the
+mean approx-KL exceeds 1.5×target_kl. Adopting it should, in principle,
+provide an additional safety net atop PPO's clipping objective.
+
+**Configuration.** v3 hyperparameters + `target_kl=0.015`,
+`batch_size=128`, `n_epochs=6`, augmentation enabled.
+
+**Failure mode.** KL early stopping fired in 80% of update iterations,
+causing the average completed-epoch count to fall to 2.36/6. Effective
+update count per rollout dropped to 39% of nominal; training was severely
+under-utilising its rollout budget. Final mean return was projected to
+be substantially below v3.
+
+**Diagnosis.** The combination of larger batch (128 vs 64) and observation
+augmentation inflated the natural KL between rollout and updated policy
+beyond the 0.0225 trigger. KL early stopping is correct in principle but
+poorly calibrated in this regime.
+
+### 4.2 Failed attempt: Random-shift data augmentation (RAD-style)
+
+**Motivation.** Laskin et al. 2020 (RAD) and Yarats et al. 2021 (DrQ-v2)
+demonstrated that random-shift augmentation dramatically improves
+generalisation in pixel-based reinforcement learning. CarRacing's
+procedural track generation should benefit similarly.
+
+**Configuration.** v3 hyperparameters + augmentation only,
+`batch_size=64`, `n_epochs=10`, no KL early stopping.
+
+**Failure mode.** Training reached a peak running-mean return of +811
+at step 258K, then collapsed catastrophically over the next 125K steps,
+falling to -84 at step 383K. Policy entropy fell to 0 (fully
+deterministic) and approximate KL spiked to 0.82 within a single
+update window.
+
+**Diagnosis.** The root cause is a structural mismatch between
+augmentation and PPO: the rollout buffer stores the old log-probability
+computed on raw observations, but the updated log-probability is computed
+on augmented observations. The probability ratio is therefore evaluated
+on a different input distribution than the buffer's reference, inflating
+its variance. RAD was originally designed for SAC (an off-policy
+algorithm where this concern does not arise); naively transferring it to
+PPO requires a regulariser like DrAC (Raileanu et al. 2020) which we
+did not implement.
+
+### 4.3 Failed attempt: gamma=0.995 + 5M-step training
+
+**Motivation.** SB3 RL-Zoo's tuned CarRacing configuration uses
+gamma=0.995 (longer effective horizon, better for ~1000-step episodes),
+and CarRacing-solved checkpoints in the literature typically train for
+2-5M steps. We hypothesised this would yield improved generalisation
+without the augmentation pitfall.
+
+**Configuration.** v3 hyperparameters + gamma 0.99→0.995 + 5M total
+steps, no augmentation, no clip annealing.
+
+**Failure mode.** Training reached peak +770 at step 278K then began a
+slow decline. By step 405K, return had fallen to +599 with policy
+entropy at 0.082 and KL spikes up to 0.31 in the recent 30 iterations.
+We aborted at 8% progress.
+
+**Diagnosis.** A larger gamma propagates value information further into
+the past, increasing the magnitude of advantages and amplifying the
+size of policy updates. Combined with PPO's already-aggressive 10
+update epochs per rollout, this drove entropy collapse on the same
+mechanism we observed in the augmentation experiment. The lesson is
+that *any* refinement that increases the per-update perturbation of
+the policy — whether through input distribution shift (4.2) or through
+discount-factor amplification (4.3) — risks destabilising the long-
+horizon training trajectory under PPO's clipping-only safety net.
+
+### 4.4 What this teaches us
+
+PPO's stability is not free; it is purchased through narrow
+hyperparameter ranges. The original v3 configuration occupies a stable
+operating point because all three refinements above either remove or
+perturb the implicit assumption that ratio variance is bounded. SB3's
+production-grade defaults appear to compensate via additional
+mechanisms (running observation normalisation, adaptive clip range,
+DrAC-like augmentation regularisers) that we did not replicate. For
+this coursework we therefore submit v3 as the production model, and
+present these three negative results as evidence of the algorithm's
+brittleness to seemingly small modifications.
+
+---
+
+## Summary table
+
+| # | Challenge | Resolution | Key metric |
+|---|-----------|------------|-----------|
+| 1 | Single-env rollout: 20 sps, GPU 12 % util | AsyncVectorEnv, 8 workers | sps 20 → 95 (4.5×) |
+| 2 | Policy collapse near step 100K, entropy ~0.4 | Entropy floor 0.005 + reward floor −1.0 | min return 311 → 437 (+41 %) |
+| 3 | Final checkpoint biased toward high mean / high variance | Per-checkpoint held-out evaluation | min return 327 → 505 (+54 %) |
+
+These three resolutions together account for the difference between
+our submitted agent (mean 830.17, std 104.79, min 436.81) and the
+production SB3 PPO baseline (mean 664.32, std 173.93, min 309.40).
@@ -0,0 +1,150 @@
+# Submission Checklist
+
+最后提交前**逐项核对**，避免格式扣分。
+
+## 1. 命名格式
+
+- [ ] zip 文件名：`CW1_<学号>_<姓名拼音>.zip`
+  - 例：`CW1_2012345_ZhangSan.zip`
+- [ ] PDF 文件名：`CW1_<学号>_<姓名拼音>.pdf`
+- [ ] 学号 + 姓名拼写**全程一致**（zip / pdf / 报告封面页）
+- [ ] **PDF 不放进 zip**！分两个文件单独上传
+
+## 2. zip 内容（提交前检查）
+
+```
+CW1_<ID>_<Name>.zip
+├── README.md                       ✅
+├── requirements.txt                ✅
+├── train.py                        ✅ 单环境 legacy
+├── train_vec.py                    ✅ 主训练脚本
+├── train_sb3_baseline.py           ✅ SB3 基线
+├── evaluate.py                     ✅ 评估脚本
+├── scan_checkpoints.py             ✅ checkpoint 扫描
+├── src/
+│   ├── __init__.py
+│   ├── env_wrappers.py
+│   ├── vec_env_wrappers.py
+│   ├── networks.py
+│   ├── rollout_buffer.py
+│   ├── vec_rollout_buffer.py
+│   ├── ppo_agent.py
+│   ├── eval_utils.py
+│   └── utils.py
+├── notebooks/
+│   ├── 01_explore_env.ipynb
+│   ├── 02_test_network.ipynb
+│   ├── 03_test_buffer.ipynb
+│   ├── 04_test_ppo.ipynb
+│   └── 05_evaluate.ipynb
+├── models/
+│   └── ppo_final.pt                ⭐ 最佳 checkpoint，重命名后唯一一个
+├── runs/
+│   └── vec_main_v3/                ⭐ 主训练 TensorBoard 日志
+└── docs/                           ✅ 报告素材（可全部保留）
+    ├── step00_skeleton.md
+    ├── step01_env_exploration.md
+    ├── ...
+    ├── step07_evaluation.md
+    ├── issues_and_fixes.md
+    ├── report_outline.md
+    ├── eval_summary.json
+    ├── checkpoint_scan_*.json
+    ├── fig_eval_bar.png
+    ├── fig_training_curves.png
+    └── demo.mp4
+```
+
+## 3. zip 不应包含的东西（提交前删除）
+
+- [ ] `__pycache__/` 目录（src/ 下可能有，删掉）
+- [ ] `*.pyc` 文件
+- [ ] `.ipynb_checkpoints/` 目录
+- [ ] `runs/smoke_test/`、`runs/smoke_v2/`、`runs/n8_speed_test/`、`runs/vec_smoke*/` 等无用日志
+- [ ] `models/main_v1_baseline/`、`models/smoke_*/`、`models/n8_speed_test/` 等无用 checkpoint
+- [ ] `models/vec_main_v3/iter_*.pt` 中除最佳外的所有中间 checkpoint
+- [ ] `anaconda_projects/` 等 IDE 自动产生目录（如果存在）
+
+### 一键清理命令
+
+```powershell
+cd D:\projects\CW1_xxx
+
+# 1. 把最佳 checkpoint 复制为 ppo_final.pt
+Copy-Item models\vec_main_v3\<最佳iter>.pt models\ppo_final.pt
+
+# 2. 删除中间 checkpoints
+Remove-Item -Recurse -Force models\vec_main_v3
+Remove-Item -Recurse -Force models\main_v1_baseline -ErrorAction SilentlyContinue
+Remove-Item -Recurse -Force models\smoke_test, models\smoke_v2, models\n8_speed_test, models\vec_smoke -ErrorAction SilentlyContinue
+
+# 3. 删除无用 runs
+Remove-Item -Recurse -Force runs\smoke_test, runs\smoke_v2, runs\n8_speed_test, runs\vec_smoke, runs\vec_smoke_v3, runs\vec_smoke_v3b -ErrorAction SilentlyContinue
+
+# 4. 把 vec_main_v3 重命名成 main（提交时更清晰）
+Move-Item runs\vec_main_v3 runs\main
+
+# 5. 删除 __pycache__ 和 .ipynb_checkpoints
+Get-ChildItem -Recurse -Force -Include "__pycache__",".ipynb_checkpoints" | Remove-Item -Recurse -Force
+Get-ChildItem -Recurse -Filter "*.pyc" | Remove-Item -Force
+```
+
+## 4. PDF 报告内容核对
+
+- [ ] **第一页 cover page**：含学生 ID
+- [ ] **字数 ≤ 3000**（不含 References 和 Appendix）
+- [ ] 5 个 section 全有：Introduction / Methodology / Implementation Details /
+      Results and Analysis / Conclusion
+- [ ] **3 张关键图**：训练曲线、评估柱状图、SB3 对比（已在 fig_training_curves.png）
+- [ ] **超参数表**（Table 1 in Section 3.3）
+- [ ] **网络架构图**（手绘或 PowerPoint 画）
+- [ ] **References** 至少 3-5 篇（PPO + GAE + Gymnasium 文档）
+- [ ] **PDF 字体清晰**，所有图表 axis label / legend 都可读
+- [ ] **PDF 文件可在另一台电脑打开**（不要损坏）
+
+## 5. 代码可复现性（致关键）
+
+提交前在**不同目录**或**不同电脑**做这个测试：
+
+```powershell
+# 假设你解压 zip 到一个新位置
+cd C:\test_dir\CW1_<ID>_<Name>
+
+# 1. 装依赖
+pip install -r requirements.txt
+
+# 2. 加载模型测试
+python -c "from src.ppo_agent import PPOAgent; agent = PPOAgent(); agent.load('models/ppo_final.pt'); print('OK')"
+
+# 3. 跑评估（最少 5 episodes）
+python evaluate.py --ckpt models/ppo_final.pt --episodes 5
+```
+
+如果以上 3 步全过，提交内容**复现性 OK**。
+
+## 6. 学术诚信
+
+- [ ] `src/` 下**没有**任何 `from stable_baselines3 import` 语句
+  - 验证：`Get-ChildItem src\ -Recurse -Filter *.py | Select-String "stable_baselines3"`
+- [ ] `train_sb3_baseline.py` 在报告里**明确标记为 baseline only**
+- [ ] 所有外部代码灵感（CleanRL、PPO 论文、37 details 博客）在报告 References 里**列出**
+- [ ] 报告封面"yes" 同意匿名教学使用（视个人意愿）
+
+## 7. 学习 Mall 上传后
+
+- [ ] **下载 zip 和 pdf**，验证文件完整未损坏
+- [ ] 在干净电脑上重新打开 PDF 看一眼
+- [ ] 截图保存提交确认页（防系统崩溃）
+
+## 8. 时间节点（截止 2026-05-04 23:59）
+
+- 至少 **48 小时前**（即 2026-05-02 中午）完成所有内容
+- **不要** 拖到截止当天，Learning Mall 临近截止经常上传失败
+- 留 1-2 天缓冲修 bug / 改报告
+
+## 9. 紧急情况备选
+
+- 如果 vec_main_v3 训练崩溃 → 使用 `runs/main_v1_baseline/` + `models/main_v1_baseline/` 数据
+  + 报告里诚实说明 (305K 步早期停止)
+- 如果 SB3 baseline 没跑出来 → 报告 Section 4.3 删掉对比，改成"plan to compare in future work"
+- 如果 PDF 超字数 → 删 Implementation Details 里的次要细节，保留 Methodology 和 Results