Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,110 @@
+---
+license: mit
+task_categories:
+  - reinforcement-learning
+  - video-classification
+tags:
+  - world-models
+  - pixel-art
+  - synthetic
+  - action-conditioned
+size_categories:
+  - 1M<n<10M
+---
+
+# Small Worlds: a generated pixel-platformer corpus
+
+**2,025,385 frames** of a single controllable agent in a deterministic 2D
+platformer, with per-frame actions, rewards, episode boundaries and task labels.
+Nothing here was scraped: every frame was rendered by a simulator written for the
+purpose, which makes the coverage structure a *controlled variable*.
+
+Built for research on world models and on hallucination in generative dynamics
+models. Accompanying report: *Small Worlds: Subject-Scale Failure in
+Reconstruction-Trained World-Model Tokenizers*.
+
+## Why this corpus exists
+
+Two properties are hard to obtain from recorded gameplay and are exact here:
+
+1. **A known finite palette.** The renderer emits exactly **21 colours**, so
+   reconstruction can be scored as exact per-pixel agreement and *restricted to
+   any semantic subset* — the agent's own colours, for instance. This is what
+   makes the paper's central measurement possible.
+2. **Coverage as a dial.** Each episode targets one coin drawn from a skewed
+   distribution and ends when it is collected, so a goal is a *task* and the
+   uppermost platform is visited in ~3% of episodes by construction.
+
+The agent occupies a measured **1.30%** of frame pixels.
+
+## Splits
+
+| split | episodes | frames | size | purpose |
+|---|---|---|---|---|
+| `train` | 35,000 | 2,025,385 | 0.75 GB | training |
+| `val` | 2,400 | 136,620 | 0.05 GB | evaluation |
+| `test` | 2,400 | 139,157 | 0.05 GB | held out entirely |
+| `probe_ledge` | 2,400 | 364,684 | 0.13 GB | 100% low-coverage region |
+
+Seed blocks are widely separated and verified disjoint.
+
+## Format
+
+One `.npz` per shard, with a `.json` of per-shard statistics beside it.
+
+| field | shape | dtype | notes |
+|---|---|---|---|
+| `frames` | (N, 128, 128, 3) | uint8 | native resolution, no resampling |
+| `actions` | (N,) | uint8 | 0–5; 255 marks a terminal step |
+| `action_vecs` | (N, 16) | float32 | zero-padded continuous encoding |
+| `action_mask` | (16,) | float32 | 3 valid dimensions |
+| `rewards` | (N,) | float32 | coin +1, crate +2, door +5 |
+| `ep_id` | (N,) | int32 | episode boundaries |
+| `goal` | (N,) | uint8 | task label; enables per-task resampling |
+| `terminal` | (N,) | bool | |
+
+Frames are stored once per episode; `frames[i+1]` is the successor within an
+episode and `ep_id` marks boundaries. Flat pixel art deflates well — **368
+bytes/frame** against 49,152 raw.
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+p = hf_hub_download("maxmill/small-worlds-pixel-platformer", "train/shard_0000.npz", repo_type="dataset")
+d = np.load(p)
+frames, actions, rewards = d["frames"], d["action_vecs"], d["rewards"]
+```
+
+## Coverage structure
+
+Episode share and frame share are deliberately mismatched, reproducing the
+heavy-tailed structure of larger corpora:
+
+| task | episode share | frame share | ratio |
+|---|---|---|---|
+| ground | 31.9% | 5.3% | 0.17 |
+| P1 | 31.1% | 37.5% | 1.21 |
+| P2 | 31.0% | 41.6% | 1.34 |
+| **P3 (rare)** | **6.0%** | 15.5% | 2.57 |
+
+## Behaviour policies
+
+| policy | share |
+|---|---|
+| scripted (routes over a physics-derived hop graph) | 40% |
+| scripted + 20% random | 25% |
+| scripted + 50% random | 15% |
+| uniform random | 15% |
+| explorer (biased to the rare platform) | 5% |
+
+## Measured properties
+
+- **Redundancy**: 25.4% of frames unique (mean 3.94 repeats) → ~515k distinct frames
+- **Intrinsic dimensionality**: a frame is a deterministic function of ~26 bits of state
+- **Palette**: exactly 21 colours; 9 belong to the agent
+- **Versioning**: every shard carries `action_space_version` and `art_version`
+
+## Licence
+
+MIT.