---
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](https://github.com/maxgfr/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`](https://github.com/maxgfr/skycmd/commit/bd5a64a1c3c2dffbe74e6a2bad7409da86fed58b).
Licences of every set: [DATA_LICENSES.md](https://github.com/maxgfr/skycmd/blob/main/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` | — |

```python
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.