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license: mit
task_categories:
- reinforcement-learning
tags:
- inventory-management
- supply-chain
- reinforcement-learning
- operations-research
- gymnasium
pretty_name: gym-invmgmt trained agent weights
---
# gym-invmgmt trained agent weights
This dataset repository hosts the trained agent checkpoints and normalization statistics for the `gym-invmgmt` benchmark release.
Contents are stored under `data/models/` and are intended to be downloaded into the same path in the GitHub repository.
## Related repositories
- Paper: [arXiv:2605.11355](https://arxiv.org/abs/2605.11355)
- PyPI package: [gym-invmgmt](https://pypi.org/project/gym-invmgmt/)
- Final paper/code repository: [r2barati/gym-invmgmt-paper](https://github.com/r2barati/gym-invmgmt-paper)
- Standalone Gymnasium environment package: [r2barati/gym-invmgmt](https://github.com/r2barati/gym-invmgmt)
## Included
- Stable-Baselines3 PPO/SAC checkpoints (`.zip`)
- Imitation-learning / DAgger / GNN-IL checkpoints (`.zip`)
- Matching VecNormalize statistics (`.pkl`)
- `models_manifest.json` with SHA-256 checksums
## Not included
- Third-party Qwen GGUF LLM weights are not re-hosted here. Use the original model source or place the expected file at `data/models/qwen2.5-1.5b-instruct-q4_k_m.gguf` for optional LLM reruns.
- Public retail datasets are not re-hosted here. Use the repository download script and source-specific licenses.
## Download
From the GitHub repository root:
```bash
bash download_weights.sh
```
or with Python:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id='rezabarati/gym-invmgmt-weights',
repo_type='dataset',
local_dir='.',
allow_patterns=['data/models/*', 'models_manifest.json'],
)
```
## Citation
If you use these trained checkpoints, please cite:
```bibtex
@misc{barati2026gyminvmgmt,
title = {gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods},
author = {Barati, Reza and Hu, Qinmin Vivian},
year = {2026},
eprint = {2605.11355},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
```
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