MiniJEV · Snake
A tiny decision model (≈0.5M parameters, 2 MB) that plays Snake on a 10×10 board.
It is not a language model and generates no text. At each step it scores the
available choices (move up, move down, move left, move right) and returns a
calibrated probability for each one.
It is the first benchmark of MiniJEV. The same core (state + question +
dynamic list of choices → choice + calibrated confidence) is used for text decisions
in markuz89/minijev-general.
Results (200 games, 10×10, max score 97)
| metric | value |
|---|---|
| average score | 88.6 |
| median score | 97 (board filled) |
| games won (board filled) | 73.5 % |
| deaths | 19 % |
| stopped for starvation | 7.5 % |
| fatal moves | 0.02 % of decisions |
For comparison, the search-based oracle the model was first distilled from scores 95 on average and wins 38 % of games. The model wins more often but has a lower average score.
Training pipeline:
- Supervised distillation from the oracle.
- DAgger.
- PPO (two rounds, γ = 0.995, on an RTX 4090).
- The choices were then renamed from
UP/DOWN/…tomove up/…without retraining. Only the choice encoder was re-fit, and the probabilities are identical (max difference 4e-7).
Usage
git clone https://huggingface.co/markuz89/minijev-snake && cd minijev-snake/code
pip install -e ".[train]" # needs torch; ONNX export is available for torch-free inference
from minijev.inference.runtime import MiniJEV
dec = MiniJEV.load("..") # the cloned repo folder (or the repo id)
state = dec.task.example_states(1)[0] # {"body": [...], "food": (x, y), "direction": ...}
d = dec.decide(state) # question and choices default to the Snake task
print(d.choice, d.confidence, d.scores) # e.g. "move left" 0.999 {...}
Files
model.safetensors: weights (no pickle)config.json: architecture, task config, calibration temperature, metricscode/: theminijevpackage and the Snake environment needed to run it
Limits
The model was trained only on 10×10 boards. Inputs are the grid channels of the MiniJEV Snake environment, and it is not a general game-playing agent.
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