Datasetpublic

VAGOsolutions/SauerkrautLM-Doom-MultiVec-31k

SauerkrautLM-Doom-MultiVec-31k 31,645 human gameplay demonstration frames for training the SauerkrautLM-Doom-MultiVec-1.3M DOOM action classifier. This dataset was recorded by a human player in VizDoom's SPECTATOR mode across 4 recording sessions totaling approximately 2 hours of gameplay in the defend_the_center scenario. Each frame includes the ASCII game view, real VizDoom depth buffer data, and soft action labels derived from keyboard input. Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/VAGOsolutions/SauerkrautLM-Doom-MultiVec-31k.

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Dataset Card

SauerkrautLM-Doom-MultiVec-31k

31,645 human gameplay demonstration frames for training the [SauerkrautLM-Doom-MultiVec-1.3M](https://huggingface.co/VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M) DOOM action classifier.

This dataset was recorded by a human player in VizDoom's SPECTATOR mode across 4 recording sessions totaling approximately 2 hours of gameplay in the defend_the_center scenario. Each frame includes the ASCII game view, real VizDoom depth buffer data, and soft action labels derived from keyboard input.

Dataset Structure

Each sample contains:

Field	Type	Description
`text`	string	40x25 ASCII frame (~1024 characters), brightness-encoded
`depth_bins`	list[int]	VizDoom depth buffer quantized to 16 bins per token position
`scores`	list[float]	4-dim soft action scores: [shoot, moveforward, turnleft, turn_right]
`input_ids`	list[int]	Pre-tokenized with 75-token character-level vocabulary
`attention_mask`	list[int]	Attention mask aligned to input_ids
`depth_ids`	list[int]	Depth bin IDs aligned to token positions (16 = no depth / padding)

Soft Action Scores

Action labels are soft distributions, not hard one-hot labels. When the human presses multiple keys simultaneously (e.g., forward + shoot), both actions receive high scores (0.85), while inactive actions receive a baseline of 0.05. This provides richer supervision for KL-divergence training.

ASCII Encoding

Each frame uses 10 brightness characters: " .:-=+*#%@" (dark to bright). Bright characters indicate nearby solid objects; dark characters indicate distant or empty areas. Row separators (\n) preserve the 2D spatial layout.

Recording Setup

Setting	Value
Scenario	`defend_the_center` (circular arena, enemies from all directions)
Resolution	640x480 with HUD enabled
Frame skip	4 (one sample per 4 game tics, ~114ms real-time)
Controls	Native DOOM keyboard (arrow keys + Ctrl)
Actions	4 discrete: shoot, moveforward, turnleft, turn_right
Depth source	VizDoom depth buffer, quantized to 16 bins
Recording sessions	4 sessions, 80+ episodes, ~2 hours total
Total frames	31,645

Usage

python

from datasets import load_dataset

dataset = load_dataset("VAGOsolutions/SauerkrautLM-Doom-MultiVec-31k")
train = dataset["train"]

print(f"Samples: {len(train)}")
print(f"Features: {list(train.features.keys())}")

# Inspect a sample
sample = train[0]
print(f"ASCII frame length: {len(sample['text'])} chars")
print(f"Action scores: {sample['scores']}")
print(f"Depth bins (first 10): {sample['depth_bins'][:10]}")

Train with this dataset

bash

# Clone the project
git clone https://github.com/VAGOsolutions/doom-multivec.git
cd doom-multivec
pip install -e ".[dev]"

# Train the classifier
python scripts/train_classifier.py \
    --data VAGOsolutions/SauerkrautLM-Doom-MultiVec-31k \
    --output output/my-model \
    --epochs 10 \
    --batch-size 32 \
    --lr 3e-4

Associated Model

This dataset was used to train [SauerkrautLM-Doom-MultiVec-1.3M](https://huggingface.co/VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M), a 1.3M parameter ModernBERT-Hash classifier that achieves 178 frags in 10 episodes of VizDoom's defend_the_center, outperforming GPT-4o-mini, Nemotron-120B, Qwen3.5-27B, and Gemini Flash Lite combined.

Citation

bibtex

@misc{SauerkrautLM-Doom-MultiVec,
  title={SauerkrautLM-Doom-MultiVec-1.3M: Playing DOOM with 1.3M Parameters},
  author={David Golchinfar and Daryoush Vaziri and Alexander Marquardt},
  url={https://huggingface.co/VAGOsolutions/SauerkrautLM-Doom-MultiVec-1.3M},
  year={2026}
}

License

Apache 2.0 License.

DOOM is a registered trademark of id Software LLC. This project is not affiliated with or endorsed by id Software.