---
license: cc-by-4.0
tags:
  - reinforcement-learning
  - magic-the-gathering
  - alphazero
  - mcts
  - self-play
  - xmage
  - 17lands
---

# DraftZero experiment #1: one agent for all of MTG Foundations limited

One [MageZero](https://github.com/WillWroble/MageZero) agent trained by self-play to play
**any** Foundations (FDN) limited deck, instead of one agent per deck. Every game drew both
decks at random from 28,366 top-player decks built from 17lands data.

This release has the checkpoints, the full deck pool, every game played, and the report.
It's a **baseline and a set of pretrained opponents** for anyone training limited agents,
not a strong player.

## Results

| Measure | Result |
|---|---|
| Training | 2,507 games over 34 generations, one RunPod L40S, ~34 h, ~$28 |
| Gen 33 vs raw search (same search, no network), final eval | **110/197 (55.8%, 95% CI 49–63%)** |
| Gen 33 vs gen 10, final eval | **96/196 (49.0%, 95% CI 42–56%)**: training plateaued around gen 10 |
| All milestone evals vs raw search, pooled | 137/238 (57.6%, 95% CI 51–64%) |
| Card-value agreement with 17lands (Spearman ρ, commons, gens 10+) | 0.28 |

The network adds a modest edge over raw search. It isn't yet strong enough for its
self-play card statistics to be trusted: premium removal plays at 45–46% against 58% on
17lands. The full analysis is in [`report.md`](report.md).

## Contents

| Path | What |
|---|---|
| `checkpoints/gen33.pt.gz` | Final checkpoint |
| `checkpoints/gen10.pt.gz` | Where strength plateaued, and the final eval's second opponent |
| `checkpoints/gen0.pt.gz` | First trained checkpoint, from 96 heuristic-search bootstrap games |
| `decks/FDN_top_player_decks.tar.gz` | All 31,516 decks as XMage `.dck` files, plus `decks.jsonl` (per-deck cards, colors, player win-rate bucket) |
| `decks/decks.tsv` | Train/eval split, by draft: 28,366 train, 3,150 eval |
| `decks/eval_pairs_milestone.tsv` | The fixed 40-game eval (20 deck pairs × both seatings) played at every milestone |
| `decks/eval_pairs_final.tsv` | The 200 raw-search games of the final eval (100 pairs, 192 decks) |
| `run/games.jsonl` | Every game: both decks, colors, cards drawn, winner |
| `run/metrics.jsonl` | Every metric the loop logged, per generation |
| `run/final_eval.json`, `run/deck_records.tsv`, `run/run.json` | Final eval, per-deck records, run configuration and provenance |
| `dashboards/` | The run's training dashboard and format dashboard (open `index.html`) |
| `report.md` | The experiment report |

## Using the checkpoints

The model is MageZero's 2-layer transformer (d_model 512). Each checkpoint carries its own
feature vocabulary. Playing games with them needs three things:

| Needed | Where |
|---|---|
| MageZero **0.1.0**: upstream v0.1.0-alpha plus 7 fork commits | `pip install "magezero @ git+https://github.com/danieljbrooks/MageZero@bcc76de"` |
| The **action vocabulary** the policy heads index into | [`assets/vocab/FDN_SPG.tsv`](https://github.com/danieljbrooks/draft-zero/blob/exp1-fdn-generalist/assets/vocab/FDN_SPG.tsv) in draft-zero; point `MZ_ACTION_VOCAB` at it |
| The **generalist XMage build**: upstream XMage plus one commit that emits actions in that vocabulary | [`danieljbrooks/mage`](https://github.com/danieljbrooks/mage), branch [`exp1-fdn-generalist`](https://github.com/danieljbrooks/mage/tree/exp1-fdn-generalist) (commit `5a32441c` on WillWroble/mage `2f35d9f7`) |

Whether the checkpoints load under MageZero v0.2.0 is untested. None of this has been run
end to end outside the training harness yet.

The training harness is [draft-zero](https://github.com/danieljbrooks/draft-zero) (MIT), tag
[`exp1-fdn-generalist`](https://github.com/danieljbrooks/draft-zero/tree/exp1-fdn-generalist).

## Data and license

Every deck, and every human reference number in the report, comes from
**[17lands](https://www.17lands.com/)**' public FDN Premier Draft game data
([public datasets](https://www.17lands.com/public_datasets)), which 17lands licenses under
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). The pool is every deck whose
player sits in the ≥60% win-rate bucket. This release is also **CC BY 4.0**. If you use it,
credit 17lands, and this release.

## Credits

- **[17lands](https://www.17lands.com/)** for the public data, and the players who share it.
- **Will Wroble** for [MageZero](https://github.com/WillWroble/MageZero), and for advice on
  this run.
- **[chrismaghuhn](https://github.com/chrismaghuhn)** for advice on compute and on
  performance ([WillWroble/MageZero#3](https://github.com/WillWroble/MageZero/issues/3)).
- The **[XMage](https://github.com/magefree/mage)** project for the rules engine.

Author: Daniel Brooks. Run `2026-09-22_02-46-01`, report written 2026-09-23.