---
library_name: onnxruntime
license: mit
tags:
  - skat
  - card-game
  - onnx
language:
  - en
---

# Skat ML Models

Models for playing the German card game [Skat](https://en.wikipedia.org/wiki/Skat_(card_game)), trained on ~9 million games from the International Skat Server (ISS).

## Models

| Model | Architecture | Description | Size |
|-------|-------------|-------------|------|
| `bidding_dense` | Dense ResNet | Pre-skat bidding evaluation | ~9 MB |
| `bidding_transformer` | Transformer | Pre-skat bidding with attention | ~14 MB |
| `game_eval_dense` | Dense ResNet | Post-skat game type selection | ~9 MB |
| `game_eval_transformer` | Transformer | Post-skat evaluation with attention | ~13 MB |
| `card_play_transformer` | Transformer | Card play selection | ~72 MB |

## Gameplay Phases

Skat has three decision phases, each with its own model:

- **Bidding** — "Can I win at bid level X?" Evaluates win probability across all 63 bid levels.
- **Game selection** — "Which discards + game type maximize win probability?" Evaluates after winning the bid.
- **Card play** — "Which card should I play?" Selects from legal cards during tricks.

## Model Details

| Model | Input | Output |
|-------|-------|--------|
| BiddingEvaluator | 35 features (10 cards + position) | 63 × 2 win probs (pickup/hand) |
| BiddingTransformer | 10 card indices + position | 63 × 2 win probs (pickup/hand) |
| GameEvaluator | 75 features (hand + skat + context) | 1 win probability |
| GameEvaluatorTransformer | Card indices + context | 1 win probability |
| CardPlayTransformer | Sequence of moves | 32 card logits |

## Interface Specification

The `interface/` directory defines the contract for using these models:

- **`feature_spec.json`** — Card encoding, game type encoding, input/output tensor shapes
- **`test_vectors.json`** — Reference inputs and expected outputs for validation

Use these to ensure your implementation encodes inputs correctly.

## Usage

Load with ONNX Runtime in any supported language (Python, Java, C++, etc.):

```python
import onnxruntime as ort

session = ort.InferenceSession("bidding_transformer.onnx")
```

## Training

Models were trained with rating-weighted loss using ISS player ELO ratings, so decisions from stronger players contribute more to the learned policy.

For training code and details, see the [GitHub repository](https://github.com/avaskys/skat-ml-models).

## License

MIT