| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - decision-model |
| - reinforcement-learning |
| - snake |
| - minijev |
| library_name: minijev |
| --- |
| |
| # 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`](https://huggingface.co/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: |
| 1. Supervised distillation from the oracle. |
| 2. DAgger. |
| 3. PPO (two rounds, γ = 0.995, on an RTX 4090). |
| 4. The choices were then renamed from `UP/DOWN/…` to `move up/…` without retraining. Only the choice encoder was re-fit, and the probabilities are identical (max difference 4e-7). |
|
|
| ## Usage |
|
|
| ```bash |
| 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 |
| ``` |
|
|
| ```python |
| 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, metrics |
| - `code/`: the `minijev` package 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. |
|
|
