Datasets:

rezabarati
/

gym-invmgmt-weights

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
PyPI package: gym-invmgmt
Final paper/code repository: r2barati/gym-invmgmt-paper
Standalone Gymnasium environment package: 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 download_weights.sh

or with 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:

@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}
}