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Datasets:
maxmill
/
small-worlds-pixel-platformer

Tasks:
Reinforcement Learning
Video Classification
Size:
1M<n<10M
Tags:
world-models
pixel-art
Synthetic
action-conditioned
License:
Dataset card Files
xet
 Community
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
shard: string
action_space_version: int64
art_version: int64
episodes: int64
frames: int64
steps: int64
bytes: int64
bytes_per_frame: double
reward_total: double
goal_episodes: struct<0: int64, 1: int64, 2: int64, 3: int64>
  child 0, 0: int64
  child 1, 1: int64
  child 2, 2: int64
  child 3, 3: int64
goal_frames: struct<0: int64, 1: int64, 2: int64, 3: int64>
  child 0, 0: int64
  child 1, 1: int64
  child 2, 2: int64
  child 3, 3: int64
goal_success: struct<0: double, 1: double, 2: double, 3: double>
  child 0, 0: double
  child 1, 1: double
  child 2, 2: double
  child 3, 3: double
policy_counts: struct<scripted: int64, scripted_eps0.2: int64, scripted_eps0.5: int64, random: int64, explorer: int (... 3 chars omitted)
  child 0, scripted: int64
  child 1, scripted_eps0.2: int64
  child 2, scripted_eps0.5: int64
  child 3, random: int64
  child 4, explorer: int64
seconds: double
task_frame_share: struct<0: double, 1: double, 2: double, 3: double>
  child 0, 0: double
  child 1, 1: double
  child 2, 2: double
  child 3, 3: double
gb: double
task_episode_share: struct<0: double, 1: double, 2: double, 3: double>
  child 0, 0: double
  child 1, 1: double
  child 2, 2: double
  child 3, 3: double
shards: int64
to
{'shards': Value('int64'), 'episodes': Value('int64'), 'frames': Value('int64'), 'bytes': Value('int64'), 'gb': Value('float64'), 'bytes_per_frame': Value('float64'), 'task_episode_share': {'0': Value('float64'), '1': Value('float64'), '2': Value('float64'), '3': Value('float64')}, 'task_frame_share': {'0': Value('float64'), '1': Value('float64'), '2': Value('float64'), '3': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              shard: string
              action_space_version: int64
              art_version: int64
              episodes: int64
              frames: int64
              steps: int64
              bytes: int64
              bytes_per_frame: double
              reward_total: double
              goal_episodes: struct<0: int64, 1: int64, 2: int64, 3: int64>
                child 0, 0: int64
                child 1, 1: int64
                child 2, 2: int64
                child 3, 3: int64
              goal_frames: struct<0: int64, 1: int64, 2: int64, 3: int64>
                child 0, 0: int64
                child 1, 1: int64
                child 2, 2: int64
                child 3, 3: int64
              goal_success: struct<0: double, 1: double, 2: double, 3: double>
                child 0, 0: double
                child 1, 1: double
                child 2, 2: double
                child 3, 3: double
              policy_counts: struct<scripted: int64, scripted_eps0.2: int64, scripted_eps0.5: int64, random: int64, explorer: int (... 3 chars omitted)
                child 0, scripted: int64
                child 1, scripted_eps0.2: int64
                child 2, scripted_eps0.5: int64
                child 3, random: int64
                child 4, explorer: int64
              seconds: double
              task_frame_share: struct<0: double, 1: double, 2: double, 3: double>
                child 0, 0: double
                child 1, 1: double
                child 2, 2: double
                child 3, 3: double
              gb: double
              task_episode_share: struct<0: double, 1: double, 2: double, 3: double>
                child 0, 0: double
                child 1, 1: double
                child 2, 2: double
                child 3, 3: double
              shards: int64
              to
              {'shards': Value('int64'), 'episodes': Value('int64'), 'frames': Value('int64'), 'bytes': Value('int64'), 'gb': Value('float64'), 'bytes_per_frame': Value('float64'), 'task_episode_share': {'0': Value('float64'), '1': Value('float64'), '2': Value('float64'), '3': Value('float64')}, 'task_frame_share': {'0': Value('float64'), '1': Value('float64'), '2': Value('float64'), '3': Value('float64')}}
              because column names don't match

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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.

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.
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Total file size:
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