---
license: cc-by-4.0
pretty_name: DraftZero self-play games (Magic The Gathering limited, Foundations)
tags:
- magic-the-gathering
- self-play
- mcts
- reinforcement-learning
- games
size_categories:
- n<1K
---

# DraftZero self-play games

Games of **Magic: The Gathering limited** (Foundations, FDN) played by [DraftZero](https://github.com/danieljbrooks/draft-zero)'s
agent against itself, with tree search at every decision. Each searched decision is stored as a training example:
the position, the search's answer and the game's result. They are what DraftZero's self-play training learns from
([docs/021](https://github.com/danieljbrooks/draft-zero/blob/main/docs/021-self-play-plan.md)).

**Status: empty for now.** Games arrive once the self-play loop starts. The layout and format below may still change
before the first upload; this card will say when they do.

## What a game is

- **The engine:** [XMage](https://github.com/magefree/mage), a full rules implementation of Magic, driven through
  [MageZero](https://github.com/WillWroble/MageZero).
- **The decks:** two decks drawn at random from top players' Foundations Premier Draft decks published by
  [17lands](https://www.17lands.com/public_datasets) (players with a 60%+ win rate).
- **The players:** a policy-value network (first trained to imitate top players' decisions from 17lands replays,
  then by self-play) searching each decision with Monte Carlo tree search over sampled versions of the hidden cards.
  Neither side sees the other's hand or decklist.
- **The budget:** the number of search simulations per decision is recorded with each game (e.g. `il_bc@100`).
- About 169 searched decisions a game; about 100 KB a game, gzipped.

## Format

Files are gzipped JSON lines. **One line is one seat's view of one game:**

| Field | Meaning |
|---|---|
| `pair` | the deck pair's index in its run |
| `swap` | whether the seats were swapped (each deck pair is played twice, once each way) |
| `seat` | `A` or `B` |
| `bot` | the player and its search budget, e.g. `il_bc@100` |
| `result` | the game's result for this seat: `1` win, `-1` loss, `null` no winner (e.g. the 50-turn cap) |
| `records` | the seat's searched decisions, in order (below) |

**Each entry of `records` is one decision:**

| Field | Meaning |
|---|---|
| `features` | the position as MageZero encodes it, from this seat's point of view (the opponent's hand hidden): sorted ids of discrete features (cards in zones with their status, life totals, turn and step, ...). Ids are XMage feature hashes (`xmage_feature_hash`, version 1) |
| `type` | the decision: `PRIORITY` (cast, play or pass), `CHOOSE_USE` (yes or no, e.g. "attack with this creature?"), `CHOOSE_TARGET` (a target, or which attacker to block) |
| `turn` | the game's turn number |
| `legal` | the legal options, as indices into DraftZero's action vocabulary ([`assets/vocab/FDN_SPG.tsv`](https://github.com/danieljbrooks/draft-zero/blob/main/assets/vocab/FDN_SPG.tsv)); for `CHOOSE_USE`, `0` is no and `1` is yes |
| `visits` | how many search simulations went to each legal option (same order as `legal`): the policy target |
| `q` | the search's value of the position for this seat, from -1 (lost) to 1 (won) |
| `heuristic` | MageZero's hand-written evaluation of the position for this seat |

Planned additions, before or with the first games: the network's prior and each option's mean value, the option
actually played, the network version, and a replay log (seeds and choices, so any future encoder can rebuild the
positions).

## Reading it

```python
import gzip, json
from huggingface_hub import hf_hub_download, list_repo_files

repo = "danbrooks/draftzero-selfplay-games"
files = [f for f in list_repo_files(repo, repo_type="dataset") if f.endswith(".jsonl.gz")]
path = hf_hub_download(repo, files[0], repo_type="dataset")
for line in gzip.open(path, "rt"):
    game = json.loads(line)
    for d in game["records"]:
        best = d["legal"][max(range(len(d["visits"])), key=d["visits"].__getitem__)]
```

DraftZero's `tools/imitation_scale/selfplay_tables.py` turns these files into training tables.

## License and credits

- **This dataset:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
- **[17lands](https://www.17lands.com/)**: every deck comes from its public datasets, licensed CC BY 4.0. Thank you
  to 17lands and the players who share their data.
- **[XMage](https://github.com/magefree/mage)**, the rules engine every game runs in, and
  **[MageZero](https://github.com/WillWroble/MageZero)** by Will Wroble, the framework DraftZero builds on.
- The code that produces the games is MIT-licensed: [danieljbrooks/draft-zero](https://github.com/danieljbrooks/draft-zero).
- Magic: The Gathering is a trademark of Wizards of the Coast. This dataset is unofficial and not affiliated with or
  endorsed by Wizards of the Coast.