README.md

metadata

license: cc-by-4.0
viewer: false
tags:
  - reinforcement-learning
  - offline-reinforcement-learning
  - unity-ml-agents
  - minari
  - causal-gpt-rl

Causal GPT-RL — Unity ML-Agents trajectories

Recorded Unity ML-Agents trajectories packaged as Minari datasets — offline-RL datasets spanning continuous and discrete action spaces. Any offline-RL method can train on them; we built them to develop Causal GPT-RL, our new approach that works across both space types. Every environment ships an expert tier; eight ship a full quality ladder (expert + calibrated medium/simple) synthesized by degrading the stock policy — Gaussian action-noise ranges for the continuous scenes, softmax-temperature ranges on the policy logits for the discrete goal games.

Tiers are keyed to Minari-normalized skill between a random-policy 0.0 anchor and the stock expert 1.0 — targets simple 0.60 / medium 0.80 / expert 1.0 (envs land at simple 0.59–0.61, medium 0.80–0.82). The degradation is not a single constant: it is drawn per episode from a calibrated range (the group/team scenes — DungeonEscape, SoccerTwos — draw per match instead), so each tier's mean hits its target while the episodes span a continuous band of skill instead of piling at one point.

Companion repos

• Model-removed Unity builds + matching stock ONNX policies: ccnets/causal-gpt-rl-unity-envs
• Causal GPT-RL policy checkpoint (Pyramids): ccnets/causal-gpt-rl-unity

Data-quality notice — action labels (2026-07-30)

All twelve datasets of the four discrete-action environments have been re-recorded and replaced. ML-Agents agents decide only every k-th physics step and Unity repeats the last decision through the gap; the collector stored a placeholder 0 for those in-between steps instead of the action Unity was actually running. The action column therefore disagreed with the policy that produced the trajectories — 72% of rows in the old PushBlock expert file. Observations, rewards, terminations and the trajectories themselves were never affected, so every published return, step-reward and tier calibration still stands.

Share of rows that had held a placeholder action, measured per tier by replaying the collector from the commit before the fix at each shipped band:

env	affected rows	status
pushblock	47–72%	all three tiers fixed
dungeon-escape	24–36%	all three tiers fixed
soccer-twos	≈6%	all three tiers fixed
pyramids	3–6%	all three tiers fixed
crawler, walker, 3dball-hard	≈1%	pending
worm	0%	unaffected — decides every step

The four continuous scenes above are still pending; worm never needed the fix. If you consume observations/rewards only, every dataset here is usable as published. If you need action labels — behavior cloning, action-conditioned models, most offline RL — the discrete environments are now correct.

Because a collection run is not bit-reproducible (agents finish episodes asynchronously, so the episode boundaries differ), the re-recorded tiers have slightly different episode/transition counts and tier metrics than the files they replaced — within ~1% and ~0.02 normalized respectively. The tables below carry the new figures.

Contents

Dataset id	Episodes	Transitions	Observation	Action
unity/crawler/expert-v0	1,048	1,000,002	Tuple(Box(126), Box(32))	Box(20, [-1, 1])
unity/crawler/medium-v0	1,129	1,000,000	Tuple(Box(126), Box(32))	Box(20, [-1, 1])
unity/crawler/simple-v0	1,344	1,000,000	Tuple(Box(126), Box(32))	Box(20, [-1, 1])
unity/pushblock/expert-v0	53,039	1,000,000	Tuple(Box(105), Box(105))	Discrete(7)
unity/pushblock/medium-v0	39,033	1,010,016	Tuple(Box(105), Box(105))	Discrete(7)
unity/pushblock/simple-v0	26,666	1,010,016	Tuple(Box(105), Box(105))	Discrete(7)
unity/soccer-twos/expert-v0	14,692	1,001,676	ego Dict wrapping Tuple(Box(264), Box(72))	ego Dict wrapping MultiDiscrete([3, 3, 3])
unity/soccer-twos/medium-v0	12,940	1,002,368	ego Dict wrapping Tuple(Box(264), Box(72))	ego Dict wrapping MultiDiscrete([3, 3, 3])
unity/soccer-twos/simple-v0	11,820	1,001,564	ego Dict wrapping Tuple(Box(264), Box(72))	ego Dict wrapping MultiDiscrete([3, 3, 3])
unity/dungeon-escape/expert-v0	35,505	1,007,206	ego Dict wrapping Tuple(Box(10), Box(360), Box(1))	ego Dict wrapping Discrete(7)
unity/dungeon-escape/medium-v0	28,314	1,012,457	ego Dict wrapping Tuple(Box(10), Box(360), Box(1))	ego Dict wrapping Discrete(7)
unity/dungeon-escape/simple-v0	22,542	1,001,594	ego Dict wrapping Tuple(Box(10), Box(360), Box(1))	ego Dict wrapping Discrete(7)
unity/3dball-hard/expert-v0	1,009	1,000,008	Tuple(Box(27), Box(18))	Box(2)
unity/3dball-hard/medium-v0	1,250	1,000,008	Tuple(Box(27), Box(18))	Box(2)
unity/3dball-hard/simple-v0	1,655	1,000,008	Tuple(Box(27), Box(18))	Box(2)
unity/pyramids/expert-v0	5,392	1,000,000	Tuple(Box(56), Box(56), Box(56), Box(4))	Discrete(5)
unity/pyramids/medium-v0	4,174	1,000,000	Tuple(Box(56), Box(56), Box(56), Box(4))	Discrete(5)
unity/pyramids/simple-v0	3,065	1,000,000	Tuple(Box(56), Box(56), Box(56), Box(4))	Discrete(5)
unity/worm/expert-v0	1,000	1,000,000	Box(64)	Box(9, [-1, 1])
unity/worm/medium-v0	1,000	1,000,000	Box(64)	Box(9, [-1, 1])
unity/worm/simple-v0	1,000	1,000,000	Box(64)	Box(9, [-1, 1])
unity/walker/expert-v0	1,458	1,000,010	Box(243)	Box(39, [-1, 1])
unity/walker/medium-v0	1,712	1,000,000	Box(243)	Box(39, [-1, 1])
unity/walker/simple-v0	2,247	1,000,000	Box(243)	Box(39, [-1, 1])

All environments, datasets, and stock policies use ML-Agents release_23. Each dataset is stored at <name>/<tier>/data/main_data.hdf5 with a sibling metadata.json (minari_version 0.5.3). SoccerTwos and DungeonEscape use an ego-agent schema — observations["agents"]["agent_0"], actions["agents"]["agent_0"] — one ego episode per physical agent; split by match_id (see docs/reproduction.md).

Loading

from pathlib import Path
from huggingface_hub import snapshot_download
import minari

snapshot_download(
    repo_id="ccnets/causal-gpt-rl-unity-datasets",
    repo_type="dataset",
    allow_patterns="worm/**",                       # one env; drop to fetch all
    local_dir=Path.home() / ".minari" / "datasets" / "unity",
)
dataset = minari.load_dataset("unity/worm/expert-v0")
# calibrated tiers: minari.load_dataset("unity/worm/medium-v0" | ".../simple-v0")
print(dataset.observation_space, dataset.action_space)

Quality ladders

Why these tiers look the way they do. In a normal RL pipeline, medium/simple data would come from early training checkpoints on the way to the expert. We don't have those checkpoints — only the final expert policy — so each tier is reduced backward from the expert: instead of one constant perturbation (a single degraded point), every episode draws its skill from a calibrated range, so the tier reproduces the distribution of skill a checkpoint spread would have.

Each ladder is a monotone skill sequence built from ONE stock policy: expert is the unmodified policy, medium/simple degrade it progressively. The degradation is calibrated so the tier mean hits its normalized target while episodes cover a continuous skill band (seed 2310000):

• continuous scenes (crawler, worm, walker, 3dball-hard) add Gaussian action noise, noise_std sampled per episode from a calibrated range;
• discrete goal games sample the policy's own action distribution via softmax(logits/T) on the exposed discrete logits — higher T picks plausible 2nd/3rd-best actions (smooth, in-distribution degradation, unlike epsilon-style uniform-random swaps which are bimodal and off-distribution). PushBlock and Pyramids each sample T per episode from a calibrated range (each episode its own skill level, like a spread of early-training checkpoints);
• cooperative discrete DungeonEscape samples a softmax temperature T per group per match from a calibrated range — all three agents in a group share one T, so each match is one coherent skill level (like an early-training checkpoint); competitive self-play SoccerTwos samples a softmax temperature T per team per match from a (wider) calibrated range — the two teams draw independently, so each match pairs two skill levels (a random gap, like cross-checkpoint league play).

Normalization is (candidate − random) / (expert − random) on each env's tier metric.

env	tier metric	medium	simple	noise method
crawler	episode return	0.82	0.60	per-episode noise_std range
worm	episode return	0.80	0.60	per-episode noise_std range
walker	episode return	0.82	0.61	per-episode noise_std range
3dball-hard	episode return	0.80	0.60	per-episode noise_std range
pushblock	step-reward	0.80	0.60	per-episode softmax temperature range
pyramids	step-reward	0.79	0.60	per-episode softmax temperature range
dungeon-escape	step-reward	0.80	0.61	per-group softmax temperature range
soccer-twos	match score	≈0.80	≈0.60	per-team softmax temperature range

soccer-twos figures are approximate: self-play stays balanced, so tier skill cannot be read off the shipped returns and comes from side-swapped calibration. Sparse-reward scenes (pyramids, pushblock, dungeon, soccer) have a physically bimodal per-episode outcome (solve vs. time-out), so the episode-return histogram barely separates the tiers. For the goal games (pushblock, pyramids, dungeon-escape) the tier metric is therefore mean step-reward = return / episode length, which grades how efficiently the goal is reached — a continuous skill signal — normalized expert = 1 / random = 0 against the shipped expert-v0 step-reward anchor (pushblock 0.3537, pyramids 0.01263, dungeon-escape 0.01936). simple targets 0.60 and the shipped tiers all sit at 0.60–0.61 (SoccerTwos, scored by side-swapped match score rather than step-reward, is approximate); medium spans 0.79–0.82.

"termination rate" below = fraction of episodes ending on the env's terminal condition — a fall for Crawler/Walker/3DBallHard, a solved push for PushBlock, maze solve ("reach") for Pyramids; Worm never terminates (truncated at 1000).

Per-environment ladder tables — exact ranges, anchors, mean returns, rates

Crawler — anchors: expert 2576.4899, random −0.88

tier	norm. return	noise_std range	mean return	term. rate	mean ep length
expert-v0	1.00	--	2576.4899	0.077	954.20
medium-v0	≈0.82	0.15–0.23	2113.0960	0.209	885.74
simple-v0	≈0.60	0.21–0.32	1557.9818	0.460	744.05

Worm — anchors: expert 1044.1063, random 0.80 (never terminates)

tier	norm. return	noise_std range	mean return	term. rate	mean ep length
expert-v0	1.00	--	1044.1063	0.000	1000.00
medium-v0	≈0.80	0.09–0.15	839.4910	0.000	1000.00
simple-v0	≈0.60	0.15–0.22	632.4776	0.000	1000.00

Walker — anchors: expert 1363.6454, random −0.49 (high intrinsic return variance)

tier	norm. return	noise_std range	mean return	term. rate	mean ep length
expert-v0	1.00	--	1363.6454	0.512	685.88
medium-v0	≈0.82	0.05–0.11	1122.0456	0.652	584.11
simple-v0	≈0.61	0.09–0.15	835.2383	0.822	445.04

3DBallHard — anchors: expert 99.1077, random 0.84 (capped balance: near-bimodal)

tier	norm. return	noise_std range	mean return	term. rate	mean ep length
expert-v0	1.00	--	99.1077	0.001	991.09
medium-v0	≈0.80	0.28–0.38	79.6258	0.341	800.01
simple-v0	≈0.60	0.31–0.44	59.7502	0.612	604.23

PushBlock — tier metric is mean step-reward (return / length); step-reward anchors: expert 0.353699 (shipped expert-v0, 53,039 ep), random ≈−0.0005 (soft). Each tier samples T per episode from a calibrated range (each episode its own skill level, like a spread of early-training checkpoints). Higher T still solves but wanders more, so the return stays high (≈4.9) while step-reward falls with efficiency. term. rate = solved.

tier	norm. step-reward	T (per episode)	mean step-reward	mean return	solved rate	mean ep length
expert-v0	1.00	--	0.353699	4.972780	≈1.00	18.85
medium-v0	0.80	U(2.1, 3.2)	0.282037	4.961591	0.997	25.88
simple-v0	0.60	U(3.25, 4.75)	0.213119	4.942463	0.996	37.88

Pyramids — tier metric is mean step-reward (return / length); step-reward anchors: expert 0.012633 (shipped expert-v0, 5,392 ep), random −0.001 (sparse: policy always times out at random). Each tier samples T per episode from a calibrated range (each episode its own skill level, like a spread of early-training checkpoints). Higher T reaches the goal less directly (longer episodes) so step-reward falls while return stays near expert. term. rate = mazes reached/solved.

tier	norm. step-reward	T (per episode)	mean step-reward	mean return	reach rate	mean ep length
expert-v0	1.00	--	0.012633	1.797730	0.9911	185.46
medium-v0	0.79	U(2.7, 4.9)	0.009784	1.742249	0.990	239.58
simple-v0	0.60	U(4.8, 7.1)	0.007201	1.636223	0.981	326.26

DungeonEscape — cooperative goal game; tier metric is mean step-reward (return / length); step-reward anchors: expert 0.019355 (shipped expert-v0, 35,505 ego ep), random ≈0.00015. A softmax temperature T is sampled per cooperative group per match from a calibrated range and shared by all three agents (one coherent skill level per match, like an early-training checkpoint); higher T reaches the exit less efficiently so step-reward falls while the group still often succeeds. Group success any(agent_return > 0) (group_metadata.jsonl) therefore stays high across tiers and no longer separates them — step-reward does.

tier	norm. step-reward	T range (per group)	mean step-reward	group success	mean ep length
expert-v0	1.00	stock (no noise)	0.019355	0.9604	28.37
medium-v0	0.80	U(1.5, 3.0)	0.015446	0.9183	35.76
simple-v0	0.61	U(3.0, 4.0)	0.011950	0.8585	44.43

SoccerTwos — tier metric is side-swapped match score vs the fixed stock policy (self-play, so mean shipped return ≈ −0.04 for both tiers and does not track skill; figures are calibration estimates). Each team draws a softmax temperature T independently per match from a (wide) calibrated range on the exposed MultiDiscrete logits, shared by its two agents — so each match pairs two independently-drawn skill levels (a random gap = cross-checkpoint league play). A tier is set by the ego team's own drawn T; the norm is that band's mean side-swapped match score vs the fixed expert. term. rate = matches always terminate.

tier	approx. norm. skill	T (per team/match)	matches	mean ep length
expert-v0	1.00	stock (no noise)	3,673	68.18
medium-v0	≈0.80	U(1.3, 2.6)	3,235	77.46
simple-v0	≈0.60	U(2.1, 3.4)	2,955	84.73

Build & reproduction steps (per-env recording recipe): see docs/reproduction.md.

Provenance & attribution

• Trajectories were generated by running Unity ML-Agents material (the builds and stock policies are Apache-2.0; see the envs repo for provenance and licenses).
• The trajectory data in this repo is an original recording licensed CC-BY-4.0. Please attribute ccnets — Causal GPT-RL and note the Unity ML-Agents source environment.

License

Creative Commons Attribution 4.0 International (CC-BY-4.0). See LICENSE.