README.md

---
license: mit
tags:
  - reinforcement-learning
  - multi-agent-reinforcement-learning
  - ad-hoc-teamwork
  - starcraft
---

# NAHT: N-Agent Ad Hoc Teamwork — experimental results

Training and evaluation logs, plus agent checkpoints, for the NAHT / POAM paper.

- **Code:** <https://github.com/carolinewang01/naht> — all training, evaluation, and
  plotting code. Every figure in the paper is reproducible from the logs here.

Also in this repo: `naht_unctrl_agents_10-18-24.zip`, a standalone 94 MB bundle of the
uncontrolled-teammate agents, if you want only the pool and not the full logs.

## Quick start

Download and extract; the archives restore the original directory layout, so the
plotting notebooks in the code repo run against the extracted tree unmodified.

```bash
huggingface-cli download carolinewang01/naht --repo-type=dataset --local-dir naht_results
cd naht_results
for f in $(find . -name '*.tar.gz' -o -name '*.tar'); do tar xf "$f" -C .; done
```

Then point `BASE_RES_PATH` in the notebooks at the extracted directory.

## Environments

| Directory | Environment | Details |
|---|---|---|
| `5v6/` | SMAC | `5m_vs_6m`, 20M env steps |
| `8v9/` | SMAC | `8m_vs_9m`, 20M env steps |
| `10v11/` | SMAC | `10m_vs_11m`, 20M env steps |
| `3sv5z/` | SMAC | `3s_vs_5z`, 20M env steps |
| `mpe-pp__ts=100_shape=0.01/` | MPE | `mpe:PredatorPrey-v0`, 100-step episodes, reward shaping 0.01, 20M env steps |
| `matrix-games__bit-3p-*/` | Bit matrix game | `bit-3p-jointactstate-v0` and `bit-3p-nostate-v0`, including the `theory_ex` encoder–decoder study |

## Experiment groups

One archive per `<env>/<experiment-group>`. Each is self-contained.

| Archive | Contents |
|---|---|
| `open_train` | NAHT training runs — POAM (`poam-*`) and the IPPO-based ablations (`ippo-*`), including critic-masking (`-cmask-`) and team-composition (`-teamcomp-`) variants |
| `ippo`, `mappo`, `iql`, `qmix`, `vdn` (and `*_ns` no-parameter-sharing variants) | Self-play training runs for the uncontrolled-teammate pool |
| `open_eval_best`, `open_eval_last` | Cross-play evaluation under N-agent ad hoc teamwork |
| `in_distr_eval` | In-distribution teammate evaluation |
| `ood_generalization`, `ood_gen_vp` | Out-of-distribution teammate generalization |
| `selfplay_mismatched_eval_best` | Self-play and mismatched-partner evaluation |
| `*__ed-tensors` | Encoder–decoder tensor dumps from the `poam_data-gathering` runs; input to `ed_vis_utils.load_data` and the within-episode ED-loss figures. Stored uncompressed (`.tar`) since `.npz` is already compressed |

## Per-run layout

Every run directory follows the [Sacred](https://github.com/IDSIA/sacred) layout:

```
<experiment-group>/
  sacred/<run>/1/config.json     # full hyperparameter config
  sacred/<run>/1/run.json        # run metadata (start/stop, host, status)
  sacred/<run>/1/info.json       # logged scalars — what vis_utils.py reads
  tb_logs/<run>/events.out.*     # TensorBoard events — what the learning-curve code reads
  models/<run>/best/             # checkpoint with the best test return
  models/<run>/<final_step>/     # checkpoint at the end of training
```

Checkpoints are PyTorch `.th` state dicts (`agent.th`, `critic.th`, `mixer.th` where
applicable, and for POAM additionally `encoder.th` / `decoder.th`), each with matching
`*_opt.th` optimizer states.

Evaluation runs record the agents they load under `trained_agents` and `unseen_agents`
in `config.json`; all of them load `"load_step": "best"`. Those are absolute paths from
the original cluster — rewrite the prefix to point at
your local copy.

## Citation

If you find our code or data useful, please cite:

```bibtex
@inproceedings{wang2024naht,
    title={N-Agent Ad Hoc Teamwork},
    author={Wang, Caroline and Rahman, Arrasy and Durugkar, Ishan and Liebman, Elad and Stone, Peter},
    booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
    year={2024}
}
```