--- 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/.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.bin.gz` (tile ownership grid), `states/t.json.gz` (players, diplomacy, units, attacks), `meta.json` (+ `gitCommit`, `hashesChecked`, `numHumans`). - `records//.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.