| --- |
| viewer: false |
| pretty_name: "One Solve, Every Step" |
| tags: |
| - reinforcement-learning |
| - actor-critic |
| - reproducibility |
| - checkpoints |
| --- |
| |
| <div align="center"> |
|
|
| # One Solve, Every Step |
|
|
| **Checkpoints for *Accelerating Actor–Critic Learning*** |
|
|
| [Code](https://github.com/Hanning99/COMP8800_Research_Project) · [Usage](#usage) |
|
|
| </div> |
|
|
| <br> |
|
|
| Saved checkpoints, simulator inputs and raw outcomes for the four benchmarks in the report. Training and evaluation code lives in the GitHub repository. |
|
|
| | Benchmark | Setting | Runs | Size | |
| |:--|:--|--:|--:| |
| | KeyCorridor | S3R2 · S3R3 · S4R3 · S5R3 | 640 | 6.56 GB | |
| | MiniGridLoCA | Reward change from AWAC, SAC and QAC starts | 480 | 8.05 GB | |
| | DeepSea | N = 10 · 20 · 30 · 40 | 520 | 3.51 GB | |
| | SecureCourier | Gate skill and route choice | 50 pairs | 10.63 GB | |
|
|
| ## Usage |
|
|
| ```bash |
| pip install -U huggingface_hub zstandard |
| huggingface-cli download denghanning/COMP8800_Research_Project --repo-type dataset --local-dir data |
| cd data |
| python archive.py verify --release DeepSea |
| python archive.py restore --release DeepSea --output DeepSea |
| ``` |
|
|
| Files are packed into 256 shards (`shards/`) indexed by `manifest.sqlite`; `SHARDS.json` lists their SHA-256 checksums. `restore` checks every file against its checksum. |
|
|
