Adapt-1 plays Super Mario Bros

Adapt-1 (Rei Labs' NeuroAdapt API) learns to clear World 1-1 of the original Super Mario Bros. This repo has three recipes that reach the flag, the code to rerun them, and the evidence from our runs.

It is a fork of fhshaik/typesafe-mario: the emulator harness and the state parser come from there. We replaced the model that picks the controls with Adapt-1 and added the learning loops, datasets and evaluations.

How it works

Adapt-1 never sees pixels. At each decision the harness sends a short row of numbers (Mario's speed, the floor ahead, the nearest enemy, the height of the obstacle in front) to a domain — one learner on Adapt-1's server, with its own rows and model — and gets back one of 8 macros, such as right_run_jump_full. After the game plays the macro out, the harness reports how far Mario got, and the domain learns from that. The warm start begins from moves recorded from a scripted player written by hand; the zero start begins from nothing.

Machina, Adapt-1's trajectory engine, works differently: it proposes the whole button sequence for an attempt at once and improves on its best attempts.

Results

Each result is a frozen evaluation: learning is switched off and Adapt-1 plays its best move. The flag is at x = 3161.

Zero start: reach per episode (dots) and frozen checkpoints (diamonds) against Records spent.

Limits. One level only: the 1-1 policies don't carry over to 2-1. Machina is deterministic and replays exactly. The two domain recipes don't: the server refits its model as rows arrive, and the same rows can fit a policy that plays differently. Machina sees Mario's position and learns one fixed sequence; the domain recipes see no position and react to what's on screen.

Watch it play

The zero-start learners playing 1-1 with learning switched off (a frozen run on 2026-10-02). At every decision the panel shows Adapt-1's score for each of the 8 moves, and at the top of each jump its keep / brake / pull back choice.

https://github.com/user-attachments/assets/a659fabd-6d9e-4ef1-aa6c-6358b53d047f

Machina replaying the button sequence it found: the command now playing, how long each button is held, and the whole 244-command sequence with a playhead.

https://github.com/user-attachments/assets/f6627986-1d24-4e72-b0b5-286f242b9384

Both clips render offline from the files in this repo: python scripts/dashboard_live.py --from-evidence runs/dashboard/zero-start-2026-10-02.json --out zero-start.mp4 and python scripts/machina_dashboard.py --journal runs/machina/logs/mario-machina-z7r-frozen-SuperMarioBros-1-1-v0-20261002T044720Z.jsonl.gz --out machina.mp4.

Run it

REPRODUCE.md has every command. The short version:

python3.13 -m venv .venv && . .venv/bin/activate
pip install -r requirements.lock.txt && pip install -e ".[mario,dev]"
pytest -q                                     # offline, no key
python scripts/pub/verify_runs.py runs        # check the evidence against its hashes
export REI_KEY=...                            # your Adapt-1 key, from app.reilabs.org/adapt-1
python scripts/machina_acquire.py --domain-id my-machina-1 --dry-run --attempts 3    # free rehearsal

Adapt-1 bills Records (each row or result you send) and Queries (each decision you ask for) separately. Get a key and see your balance at app.reilabs.org/adapt-1. Offline steps cost nothing. Coding agents: read AGENTS.md first.

Layout

src/typesafe_mario/   harness, parser, features, Adapt-1 client (stdlib only), learning loops
scripts/              the commands in REPRODUCE.md; historical/ = an exact older generator the warm-start data needs
tests/                offline tests (no key, no network)
runs/                 the evidence: machina/, warm-start/, zero-start/stage-1..5/, dashboard/, each with a sha256 index

No Nintendo ROM or other game data is included (see NOTICE). Our code is MIT.