| --- |
| license: mit |
| pretty_name: OpenFront.io archived human games |
| tags: |
| - reinforcement-learning |
| - imitation-learning |
| - games |
| --- |
| |
| # OpenFront.io archived human games |
|
|
| 285 real public multiplayer games of [OpenFront.io](https://openfront.io) |
| (10-116 humans per lobby, all maps, FFA + team modes), pulled from the |
| game's public archive API and replayed through the deterministic engine |
| ([openfrontio/OpenFrontIO](https://github.com/openfrontio/OpenFrontIO)) to |
| regenerate full state trajectories. ~420k snapshots at 10-tick (1s) cadence. |
|
|
| Every game passed the engine's embedded state-hash verification during |
| replay (games that desynced were discarded), so snapshots are bit-exact |
| reconstructions of what the players actually saw. Player identities are |
| stripped by the API; usernames remain. |
|
|
| Generated by and documented at |
| **[github.com/djmango/openfront-ai](https://github.com/djmango/openfront-ai)**. |
|
|
| ## Contents |
|
|
| - `maps/<map>.tar` — replayed snapshots, one game directory per game, same |
| format v3 as [openfront-snapshots](https://huggingface.co/datasets/djmango/openfront-snapshots): |
| `terrain.bin`, `states/t<tick>.bin.gz` (tile ownership grid), |
| `states/t<tick>.json.gz` (players, diplomacy, units, attacks), |
| `meta.json` (+ `gitCommit`, `hashesChecked`, `numHumans`). |
| - `records/<gitCommit>/<gameID>.json.gz` — the raw archived game records: |
| full per-turn **human intent logs** (every attack, boat, build, nuke, |
| alliance, betrayal, donation, emote). Replay them with |
| `datagen/replay.ts` from the repo (engine checked out at the matching |
| commit), or use them directly for behavior cloning. |
|
|
| ## Why |
|
|
| Bot self-play data ([openfront-snapshots](https://huggingface.co/datasets/djmango/openfront-snapshots)) |
| covers the state space thinly: built-in nations rarely ally, betray, nuke, |
| or invade by sea. Human games supply realistic territory shapes and the |
| full diplomatic/military action distribution — both better observation |
| coverage for encoders and (state, human action) pairs for imitation |
| pretraining. |
|
|
