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
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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)