--- license: cc0-1.0 pretty_name: "Faïence human-vs-net Azul games" tags: - game-records - azul - reinforcement-learning - human-play --- # Faïence: human-vs-net Azul games Every game played on [Faïence](https://remifabre-faience.static.hf.space/), a free browser implementation of the rules of *Azul* (Michael Kiesling) against a neural net trained by self-play, unless the player switched sharing off. This dataset is the training pile the playing page tells its players about, and it is public precisely so that a player can read everything the project collects. Records are anonymous by construction: moves, deals, which net played, and the score. No names, no accounts, no IPs, no user agents. ## Layout `games/YYYY-MM-DD/-.jsonl`, one file per ingest batch, one JSON object per line. Nothing is ever rewritten; new batches only add files. ## Record format (`faience-game/1`) Each line is a canonical record rebuilt by the collector ([RemiFabre/faience-ingest](https://huggingface.co/spaces/RemiFabre/faience-ingest)), which replayed the game in the real engine and kept it only if the recorded deals, final scores and round count reproduce exactly. Fields: - `received_at` (server clock, ISO) and `created_at` (client clock, may be null) - `seed`: the game's RNG seed (mulberry32, the page's own RNG) - `human_seat`, `human_first`: which of the two seats the human held - `net`: `{run, checkpoint, elo, params, backend}` of the opponent - `think_time_s`: the AI's search budget per move (0 = policy head only) - `moves`: `[{ply, player, action, sims?, value?}]`, `action` encoded as `source*30 + color*6 + dest` (identical in the JS and Python engines); `sims` is the positions the net searched for its move on the visitor's machine, `value` its root value on a [-1, 1] scale - `deals`: per round, the five factories plus bag and lid counts, so a record replays independently of any RNG port - `final`: `{finished, scores, outcome, rounds, exhausted}`; `finished: false` marks an abandoned game (position data with no outcome; train the value head on these with care, or not at all) ## Caveats - The collector deduplicates retried submissions by content, but a restart can rarely let a duplicate through: deduplicate by `(seed, human_seat, moves, final.scores, final.finished)` when it matters. - An abandoned game that was later resumed and finished in the same tab can appear twice: once `finished: false`, once `finished: true` with the same seed and a longer move list. Prefer the finished one. - Play strength varies wildly: these are self-selected browser visitors, from first-time players to strong club players. ## Provenance and license Collected by the [Faïence ingest Space](https://huggingface.co/spaces/RemiFabre/faience-ingest) from the [Faïence playing page](https://remifabre-faience.static.hf.space/); code and methodology in [RemiFabre/ludometer](https://github.com/RemiFabre/ludometer). The records are dedicated to the public domain (CC0). Azul is a game by Michael Kiesling; this fan research project is not affiliated with or endorsed by its publishers.