Hugging Face's logo

Datasets:
maxgfr
/
skycmd-trajectories

Tasks:
Reinforcement Learning
Modalities:
Tabular
Text
Time-series
Formats:
parquet
Languages:
English
Size:
1M - 10M
Tags:
skycmd
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
skycmd-trajectories / README.md
maxgfr's picture
maxgfr
skycmd bd5a64a1: skycmd game trajectories (models vs oracle)
1d8524c verified
preview code
|
Raw History Blame Contribute Delete
7.68 kB
metadata 
license: apache-2.0
language:
  - en
pretty_name: skycmd game trajectories (models vs oracle)
task_categories:
  - reinforcement-learning
tags:
  - skycmd
configs:
  - config_name: episodes_oracle
    data_files: data/episodes_oracle.parquet
  - config_name: episodes_s1-pico
    data_files: data/episodes_s1-pico.parquet
  - config_name: episodes_s2-nano
    data_files: data/episodes_s2-nano.parquet
  - config_name: episodes_s2-nano-doom
    data_files: data/episodes_s2-nano-doom.parquet
  - config_name: episodes_s3-pico
    data_files: data/episodes_s3-pico.parquet
  - config_name: oracle
    data_files: data/oracle.parquet
  - config_name: s1-pico
    data_files: data/s1-pico.parquet
  - config_name: s2-nano
    data_files: data/s2-nano.parquet
  - config_name: s2-nano-doom
    data_files: data/s2-nano-doom.parquet
  - config_name: s3-pico
    data_files: data/s3-pico.parquet

skycmd game trajectories (models vs oracle)

Complete episodes of the four skycmd games (Flappy Drone, Rescue Run, Obstacle Dodge, a Doom-like shooter) played by four small skycmd decision models and by the exact oracle: at every decision, the state, the model's probabilities, the oracle's soft target, the reward and the events. Generated by our Rust game cores and models: no third-party data.

Part of skycmd, tiny decision models trained from scratch in Rust that run in the browser. Written by scripts/gen-trajectories.sh (sky-arena <model> --trajectories, seed 2026) at commit bd5a64a1. Licences of every set: DATA_LICENSES.md.

One config per policy, one row per decision, in play order:

field meaning
model, game, episode, step who plays, which game, episode index, decision index in the episode
tick simulator ticks before the decision (flappy decides every tick, doom every 4)
state the compact text state the policy reads (the same as in the browser)
question, options the action question and its options, rendered as text
probs the policy's probability per option
oracle the oracle's soft target per option, computed from the full simulator
action, oracle_action the action played (argmax of probs) and the oracle's argmax
abort_probs, abort, oracle_abort dodge only: the abort question, answered by both
reward, events reward summed until the next decision, and what happened (passed, crashed, rescued, kill, hurt, ...)
vetoed the coded safety reflex replaced the action
last last decision of the episode

The episodes_* configs give one row per episode: decisions, return (every tick's reward), truncated (flappy stops alive at 50 pipes), score, max_score, success, crashed, aborted, safe_abort, vetoes, steps. Flappy has no mission to complete: its episodes stop alive at 50 pipes (truncated), so its completion is 0 and its score is the pipes passed.

What it is for. Every state a small model reaches by itself, with the expert's answer next to its own: imitation learning and DAgger (oracle is the correction), offline reinforcement learning (rewards and outcomes from five policies of very different quality, including complete failures: s1-pico and s2-nano never learned doom), calibration (confidence against correctness along an episode), and the study of compounding errors.

Per policy and game

policy game episodes decisions agreement with the oracle mean score mean return completion crash
oracle flappy 20 107,520 1.000 50 103.76 0.00 0.00
oracle rescue 200 8,704 1.000 3 35.65 1.00 0.00
oracle dodge 100 33,223 1.000 9.23 13.79 0.78 0.00
oracle doom 100 11,672 1.000 2.96 13.03 1.00 0.00
s1-pico flappy 20 107,520 0.946 50 103.76 0.00 0.00
s1-pico rescue 200 9,313 0.907 2.995 35.24 0.99 0.00
s1-pico dodge 100 34,043 0.845 9.2 13.71 0.77 0.00
s1-pico doom 100 119,835 0.000 0.02 -1.30 0.00 0.12
s2-nano flappy 20 103,942 0.943 48.3 100.22 0.00 0.05
s2-nano rescue 200 8,760 0.961 3 35.62 1.00 0.00
s2-nano dodge 100 37,561 0.765 9 13.31 0.73 0.00
s2-nano doom 100 125,314 0.000 0 -0.65 0.00 0.06
s2-nano-doom flappy 20 107,520 0.946 50 103.76 0.00 0.00
s2-nano-doom rescue 200 8,860 0.960 3 35.52 0.99 0.00
s2-nano-doom dodge 100 34,560 0.814 9.01 13.33 0.75 0.00
s2-nano-doom doom 100 11,800 0.917 2.96 13.04 1.00 0.00
s3-pico flappy 20 107,520 0.950 50 103.76 0.00 0.00
s3-pico rescue 200 8,896 0.963 3 35.55 1.00 0.00
s3-pico dodge 100 35,058 0.830 9.12 13.58 0.76 0.00
s3-pico doom 100 11,791 0.911 2.96 13.03 1.00 0.00

Sources and licences

Each config is one source and keeps its source's licence (share-alike sources stay share-alike). Attribution goes to the upstream authors; the conversion is ours.

config upstream licence how it is used rows
episodes_oracle ours: oracle apache-2.0 one row per episode: return, score, outcome 420
episodes_s1-pico ours: s1-pico apache-2.0 one row per episode: return, score, outcome 420
episodes_s2-nano ours: s2-nano apache-2.0 one row per episode: return, score, outcome 420
episodes_s2-nano-doom ours: s2-nano-doom apache-2.0 one row per episode: return, score, outcome 420
episodes_s3-pico ours: s3-pico apache-2.0 one row per episode: return, score, outcome 420
oracle ours: the games' exact oracle, which reads the full simulator (expert demonstrations) apache-2.0 every decision of 20 flappy, 200 rescue, 100 dodge and 100 doom episodes 161,119
s1-pico ours: skycmd s1-pico (ModernBERT-style, trained on flappy, rescue and dodge; never saw doom) apache-2.0 every decision of 20 flappy, 200 rescue, 100 dodge and 100 doom episodes 270,711
s2-nano ours: skycmd s2-nano (trained on flappy, rescue and dodge; never saw doom) apache-2.0 every decision of 20 flappy, 200 rescue, 100 dodge and 100 doom episodes 275,577
s2-nano-doom ours: skycmd s2-nano fine-tuned on doom with DAgger apache-2.0 every decision of 20 flappy, 200 rescue, 100 dodge and 100 doom episodes 162,740
s3-pico ours: skycmd s3-pico (the four games) apache-2.0 every decision of 20 flappy, 200 rescue, 100 dodge and 100 doom episodes 163,265

Files

file rows
data/episodes_oracle.parquet 420
data/episodes_s1-pico.parquet 420
data/episodes_s2-nano.parquet 420
data/episodes_s2-nano-doom.parquet 420
data/episodes_s3-pico.parquet 420
data/oracle.parquet 161,119
data/s1-pico.parquet 270,711
data/s2-nano.parquet 275,577
data/s2-nano-doom.parquet 162,740
data/s3-pico.parquet 163,265
files/summary.json — 
from datasets import load_dataset
ds = load_dataset("maxgfr/skycmd-trajectories", "episodes_oracle")

skycmd wrote no row with a language model: the targets come from the upstream labels (some upstream sets carry their own teacher soft labels, see their cards), the games' oracles or our generators.