license: apache-2.0
language:
- en
tags:
- synthetic
- imitation-learning
- games
- stackcraft
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- split: validation
path: validation.jsonl
Stackcraft search-teacher positions
A synthetic dataset for a deterministic, simplified falling-block placement game. 827 training positions and 215 validation positions, generated entirely by code. No human demonstrations, personal data, web scraping or human row-by-row label review are claimed. Independent agent review recomputed 16 pilot labels; that sample audit is not a review of every row.
Each row contains a visible board, current piece, one next piece, all legal actions, a teacher action, teacher action values and provenance. The board uses integer piece colors; the model encoding reduces them to occupancy because color does not affect these rules. Metadata includes seed and turn for reproduction; do not feed seed or hidden sequence metadata to the policy. Stackcraft's encoder feeds only the visible observation. Read the dataset schema and tutorial03 before use.
Related releases: source and tutorials, model, dataset, and local playable demo. The owner deferred hosted deployment. The game and Docker image need no GPU or Hugging Face subscription when run locally.
Generation
The teacher searches the current placement and exactly one preview placement. It rewards current line clears and the best next afterboard, using fixed weights for aggregate height, holes, bumpiness and cleared lines. No later pieces are available. Labels are heuristic recommendations, not proofs of optimal play.
Collection cycles random, heuristic and search-expert policies, at a 40-piece cap, to expose different board conditions. Training seeds 10000–10023 and validation seeds 20000–20005 are disjoint. Six within-training and two cross-split duplicate observations were excluded. Final test seeds 30000–30199 are reserved for complete game evaluation; there is no test-position split for training or selection.
Source commit: 70d84bd8dc5d4a60f3b96455a57d9e6f9416d109.
The bundle includes manifest.json with source/configuration and split hashes.
- train SHA256:
edd682761db95a4f25bb30a284489c54d9336a36da0a9b19d2cda860b428baa8 - validation SHA256:
eff9cdc5932e935959ac4d26dce6470d335090f7428a91930001954266d133bc
The model release includes public source under code/ and the exact regeneration
recipe in code/docs/tutorials/03-data.md. Use its Reproduce from the public
source bundle without private Git access procedure: first verify all six generator
source hashes against this dataset's manifest.json, then generate with the
original source-commit provenance. Access to the original Git repository is not
required. The procedure reproduced both JSONL hashes exactly on the study machine.
Ordinary stackcraft generate-data --output NEWDIR records the current source
identity; a new source commit deliberately changes provenance fields and therefore
file hashes even if labels are identical. A regeneration and an independent
sample-label audit preceded study training.
Limits
The study is small and entirely synthetic. Teacher bias, narrow collection seeds, short collection episodes and one-step preview limit generalization. Duplicate removal prevents exact overlap, not all structural similarity. It is not full real-time Tetris: no hold, kicks, tucks or gravity timing. Colors and piece IDs are program-generated; Stackcraft uses original game visuals and branding.
Use teacher agreement as a diagnostic and complete held-out games for outcome measurement. Do not call a model better solely because it copies these labels. Apache-2.0; see LICENSE and accompanying generation/reproduction tutorials.