Add dataset card with configs, tensor schema, and usage examples

README.md

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+---
+tags:
+  - world-model
+  - game-simulation
+  - reinforcement-learning
+  - timeseries
+license: apache-2.0
+size_categories:
+  - 100K<n<1M
+task_categories:
+  - time-series-forecasting
+pretty_name: AutoWorldModel-Bench
+configs:
+  - config_name: asteroids
+    data_files:
+      - split: train
+        path: data/asteroids/train.parquet
+      - split: validation
+        path: data/asteroids/val.parquet
+      - split: test
+        path: data/asteroids/test.parquet
+      - split: scenario
+        path: data/asteroids/scenario.parquet
+  - config_name: breakout
+    data_files:
+      - split: train
+        path: data/breakout/train.parquet
+      - split: validation
+        path: data/breakout/val.parquet
+      - split: test
+        path: data/breakout/test.parquet
+      - split: scenario
+        path: data/breakout/scenario.parquet
+  - config_name: frogger
+    data_files:
+      - split: train
+        path: data/frogger/train.parquet
+      - split: validation
+        path: data/frogger/val.parquet
+      - split: test
+        path: data/frogger/test.parquet
+      - split: scenario
+        path: data/frogger/scenario.parquet
+  - config_name: kong
+    data_files:
+      - split: train
+        path: data/kong/train.parquet
+      - split: validation
+        path: data/kong/val.parquet
+      - split: test
+        path: data/kong/test.parquet
+      - split: scenario
+        path: data/kong/scenario.parquet
+  - config_name: platformer
+    data_files:
+      - split: train
+        path: data/platformer/train.parquet
+      - split: validation
+        path: data/platformer/val.parquet
+      - split: test
+        path: data/platformer/test.parquet
+      - split: scenario
+        path: data/platformer/scenario.parquet
+  - config_name: pong
+    default: true
+    data_files:
+      - split: train
+        path: data/pong/train.parquet
+      - split: validation
+        path: data/pong/val.parquet
+      - split: test
+        path: data/pong/test.parquet
+      - split: scenario
+        path: data/pong/scenario.parquet
+  - config_name: racer
+    data_files:
+      - split: train
+        path: data/racer/train.parquet
+      - split: validation
+        path: data/racer/val.parquet
+      - split: test
+        path: data/racer/test.parquet
+      - split: scenario
+        path: data/racer/scenario.parquet
+  - config_name: snake
+    data_files:
+      - split: train
+        path: data/snake/train.parquet
+      - split: validation
+        path: data/snake/val.parquet
+      - split: test
+        path: data/snake/test.parquet
+      - split: scenario
+        path: data/snake/scenario.parquet
+---
+
+# AutoWorldModel-Bench
+
+Game state sequences for training and evaluating action-conditioned world models.
+8 classic game environments with a unified entity-based tensor schema, deterministic train/val/test/scenario splits, and 152,000 total episodes.
+
+This dataset accompanies the [AutoWorldModel-Bench](https://github.com/AutoWorldModelBench/Benchmark) benchmark for evaluating frontier coding agents on open-ended world-model research.
+
+## Dataset Structure
+
+```
+data/
+└── {game}/
+    ├── train.parquet             # 10,000 episodes
+    ├── val.parquet               #  3,000 episodes
+    ├── test.parquet              #  3,000 episodes
+    ├── scenario.parquet          #  3,000 episodes
+    └── meta.json                 # max_entities, dimensions, total_frames
+```
+
+## Games
+
+| Game | Max Entities | Total Frames | Size | Data Version |
+|------|:---:|---:|---:|:---:|
+| asteroids | 20 | 5.6M | 0.9 GB | v2 |
+| breakout | 52 | 33.0M | 0.7 GB | v2 |
+| frogger | 28 | 6.2M | 0.8 GB | v2 |
+| kong | 16 | 23.0M | 0.5 GB | v2 |
+| platformer | 24 | 10.6M | 0.3 GB | v2 |
+| pong | 5 | 42.5M | 1.7 GB | v1 |
+| racer | 6 | 21.1M | 0.9 GB | v1 |
+| snake | 48 | 15.9M | 0.2 GB | v1 |
+
+**Total: 152,000 episodes (19,000 per game), 158.0M frames**
+
+All games share a unified tensor schema: `registry_dim=34`, `state_dim=23`.
+
+Each game has 10,000 train / 3,000 val / 3,000 test / 3,000 scenario episodes.
+
+### Data Collection Policy
+
+v2 games (asteroids, breakout, frogger, kong, platformer) use a 3-policy mix for diverse behavioral coverage:
+- **Random** — uniform random actions
+- **Heuristic** — hand-crafted game-specific strategies
+- **RL checkpoint** — DQN/PPO agents at various training stages
+
+v1 games (pong, racer, snake) use a 50/50 heuristic + random mix.
+
+## Tensor Schema
+
+Each Parquet row stores one episode as serialized numpy arrays:
+
+| Tensor | Shape | Description |
+|--------|-------|-------------|
+| registry | (N, 34) | Static entity properties (collider, scale, physics) |
+| states | (T, N, 23) | Dynamic: pos_xy, alive, vel_xy, gameplay(14), pos_history(4) |
+| actions | (T, 7) | Unified action vector (7 fields across all games) |
+| globals | (T, 17) | Global game state (17 fields across all games) |
+| terminals | (T,) | Episode termination flags |
+| mutable_mask | (N,) | Which entities are prediction targets |
+| type_ids | (N,) | Global entity type IDs |
+| slot_ids | (N,) | Original 64-slot table indices |
+| rewards | (T,) | Per-frame rewards |
+
+Where N = max_entities (game-specific), T = episode length.
+
+## Usage
+
+### With the `datasets` library
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AutoWorldModel/AutoWorldModelBench", "pong")
+print(ds["train"][0].keys())
+```
+
+### Direct download with `huggingface_hub`
+
+```python
+from huggingface_hub import hf_hub_download
+import pyarrow.parquet as pq
+import numpy as np, json
+
+path = hf_hub_download(
+    "AutoWorldModel/AutoWorldModelBench",
+    "data/pong/train.parquet",
+    repo_type="dataset",
+)
+
+table = pq.read_table(path)
+row = table.to_pydict()
+states = np.frombuffer(
+    row["states"][0], dtype=row["states_dtype"][0]
+).reshape(json.loads(row["states_shape"][0]))
+```
+
+## Evaluation
+
+Models trained on this data are evaluated on multi-step open-loop rollouts at horizons {1, 10, 20}:
+
+- **Position L1**: Mean absolute error on entity (x, y) positions (lower is better)
+- **Alive F1**: F1 score on entity alive/dead classification
+- **Composite**: `0.9 * (1 - pos_l1) + 0.1 * alive_f1` (higher is better)
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{autoworldmodelbench2025,
+  title={AutoWorldModel-Bench: A Benchmark for Evaluating Coding Agents on World Model Research},
+  author={AutoWorldModel Team},
+  year={2025},
+  url={https://github.com/AutoWorldModelBench/Benchmark}
+}
+```