---
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) &nbsp;·&nbsp; [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.