Serendipity
fb09e66d09
- 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
56 lines
2.4 KiB
Markdown
56 lines
2.4 KiB
Markdown
# docs/ Index
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Documentation and report artefacts for the DTS307TC PPO coursework.
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## Final deliverables
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| File | Purpose |
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|------|---------|
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| `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. |
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| `generate_report_template.py` | Source script that produces the template. |
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**Word count** (excluding References and Appendix): 2972 / 3000.
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## Figures referenced in the report
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| File | Used in | Description |
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|------|---------|-------------|
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| `fig_architecture.png` | Fig. 1 | Shared-CNN actor-critic architecture (1.69M params) |
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| `fig_training_curves.png` | Fig. 2 | 6-panel training curves over 1.5M steps |
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| `fig_eval_bar.png` | Fig. 3 | Per-episode evaluation returns on 20 unseen seeds |
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| `fig_sb3_comparison.png` | Fig. 4 | Ours vs SB3 baseline diagnostics overlay |
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| `demo.mp4` | Submitted alongside the zip | 25-second video of the trained agent on seed 117 (return 925.40, completed at wrapped step 187) |
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## Numerical evidence
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| File | Content |
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|------|---------|
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| `eval_summary.json` | 20-episode evaluation of `models/ppo_final.pt`. Mean 830.17 ± 104.79; min 436.81; max 914.90 |
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| `eval_summary_sb3.json` | 20-episode evaluation of the SB3 baseline. Mean 664.32 ± 173.93; min 309.40; max 857.14 |
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| `checkpoint_scan_vec_main_v3.json` | Per-checkpoint evaluation table; basis for selecting `iter_0700.pt` as the submitted model |
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## Cross-cutting documents
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| File | Content |
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|------|---------|
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| `development_log.md` | Step-by-step development timeline (Days 1-9) |
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| `issues_and_fixes.md` | Three substantive engineering challenges resolved + three documented negative-result ablations (raw material for Section 3.4 and 4.4) |
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| `submission_checklist.md` | Pre-submission verification checklist |
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| `INDEX.md` | This file |
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## Project state at submission
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```
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runs/ vec_main_v3/ main 1.5M-step training
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sb3_baseline/run_1/ SB3 baseline 500K reference
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models/ ppo_final.pt submitted agent (= iter_0700.pt selected
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by held-out checkpoint scanning)
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vec_main_v3/final.pt training-end backup
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sb3_baseline/final.zip SB3 reference
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src/ eight Python modules, no SB3 imports
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notebooks/ three development notebooks (env exploration, network sanity,
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evaluation)
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```
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