TypeSafe Mario
Jev plays Super Mario Bros. from structured emulator telemetry rather than screenshots.
What Jev decides
The next controller action, whether jumping is useful now, and the immediate danger level.
The harness parses RAM and telemetry into player, trajectory, hazard, terrain, timing, and episode state. Jev chooses a legal controller macro while separate Noul and Score outputs expose jump usefulness and danger.
- Jev role
- Choose the next legal controller macro from parsed emulator state.
- Input
- Structured RAM and telemetry for player motion, hazards, terrain, reaction timing, and episode progress.
- Output
- One Choice, one Noul probability, and one Score distribution.
- Decision frequency
- By default, once every eight emulator steps.
- Fallback
- A scripted baseline is available; exact physics and timing stay in deterministic code.
How it works
Reported performance
Overview
TypeSafe Mario keeps game mechanics and timing arithmetic in code. The model receives object-centric JSON describing Mario, nearby enemies, terrain geometry, recent controls, and measured response delay, then selects one legal action.
A single request also asks whether a forward jump is useful and how dangerous the immediate situation is. Those extra signals drive the dashboard rather than overriding the controller choice. The repository contains no Nintendo ROM or game data.
- Language
- Python
- License
- Not declared
- Author
- fhshaik
Sources
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