Files
Serendipity fb09e66d09 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
2026-05-02 13:44:08 +08:00

2.4 KiB

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)