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.
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 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:
@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}
}