README.md

---
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.