docs/reproduction.md

Reproduction recipe

How the datasets in this repo were recorded from public materials. Scripts live in the GitHub repo — record with examples/unity_collection and package with collection/build_minari.py. For each ONNX-driven environment, get the matching model-removed build and stock policy from the envs repo, record each agent's per-step (observation, action, reward, termination), and preserve the action/observation structure declared by that build.

Format & provenance

All environments, datasets, and stock behavior policies use ML-Agents release_23; machine-readable provenance is in release_manifest.json. Every dataset is stored at <name>/<tier>/data/main_data.hdf5 with a sibling metadata.json (minari_version 0.5.3).

Action labels and decision periods. An ML-Agents agent requests a decision only every k-th step; Unity repeats that decision through the gap, and the agent does not appear in DecisionSteps again until the next one. For those in-between steps, record the action Unity is still running — carry the agent's last decision forward. A recorder that instead stores whatever its policy wrapper emits for an agent with no observation that step will label most transitions with a placeholder; see the dataset card's data-quality notice.

Ego-agent schema (SoccerTwos, DungeonEscape). These use observations["agents"]["agent_0"] / actions["agents"]["agent_0"]. Each physical agent is stored as one ego-centric episode because the Unity examples run one shared decentralized policy independently per agent; the wrapper exposes the multi-agent structure without granting the policy privileged access to teammates' private observations. Reconstruct a match/group by grouping the linked episodes on match_id; split by match_id, never by individual ego episode.

Quality tiers. medium/simple degrade the stock policy (seed 2310000): continuous scenes add per-episode Gaussian action noise from a calibrated range; the discrete goal games sample the policy's own logits via softmax(logits/T) and are scored by step-reward (return/length, since their episode return is bimodal) — PushBlock and Pyramids each sampling T per episode from a calibrated range (each episode its own skill level); DungeonEscape (cooperative) samples a softmax temperature T per group per match from a calibrated range — all three group agents share the match's T; competitive self-play SoccerTwos draws a per-team softmax temperature T independently per match from a (wider) range — the two teams draw independently, so each match pairs two skill levels (a random gap = league play). Exact values, anchors, and calibration notes are in the dataset card's "Quality ladders" section.

SoccerTwos

1. Build the release_23 SoccerTwos scene with both teams' model assets removed so both behavior groups are controlled externally.
2. Run the stock SoccerTwos.onnx for both teams and record both sides. The original ONNX declares two nonexistent Sentis metadata graph outputs; for ONNX Runtime, remove only those dangling output declarations (the action graph is unchanged).
3. Store the sum of per-agent reward and group_reward (goals arrive via group_reward).
4. Preserve Tuple(Box(264), Box(72)) observations and MultiDiscrete([3, 3, 3]) actions.
5. Link the four decentralized episodes per field with match_id; finish all in-flight matches after crossing one million transitions.

Expert validation: 1,001,676 transitions, 14,692 terminated episodes, 3,673 matches, 0 truncations, every match has equal linked lengths with two wins / two losses. Mean agent ep length 68.18, mean return −0.034089.

Tiers: medium/simple draw a per-team softmax temperature T ~ U(1.3, 2.6) / U(2.1, 3.4) (each team draws once per match, independently of its opponent, shared by its two agents — a per-match skill gap = cross-checkpoint league play; collect.py --team-temperature-range <lo,hi>). Self-play keeps returns balanced (both tiers ≈ 50/50 win-loss), so tier skill is set by the ego team's own drawn T and calibrated to side-swapped match-score norm ≈0.80 / ≈0.60 (expert T=1 → 1.0, random T=25 → 0). medium: 12,940 ego ep / 1,002,368 transitions / 3,235 matches; simple: 11,820 / 1,001,564 / 2,955.

DungeonEscape

1. Use the stock DungeonEscape.onnx shared POCA policy with the model removed so each agent is driven externally.
2. Record each physical agent independently; preserve its three local sensor leaves (rigid-body 10, stacked ray 360, has-key flag 1) and single Discrete(7) action.
3. Relabel each physical agent as ego path agents.agent_0. Three episodes sharing one match_id reconstruct the cooperative group (distinct local trajectories, not copies).
4. Sum reward and group_reward. Determine success only at group level — an agent that dies early may not receive a later positive group reward.
5. Split by match_id.

Expert validation: 1,007,206 transitions, 35,505 ego episodes, 11,835 groups (11,366 successes / 469 failures), 0 shape/range errors. Every match links exactly three episodes.

Tiers: medium/simple apply a per-group softmax temperature T ~ U(1.5, 3.0) / U(3.0, 4.0) (drawn once per cooperative group per match and shared by its three agents), calibrated to step-reward norm 0.80 / 0.61 against the expert step-reward anchor 0.019355 (random ≈0.00015). medium: 28,314 ego ep / 1,012,457 transitions; simple: 22,542 / 1,001,594.

PushBlock

1. Use the model-removed PushBlock build and drive all 32 agents with the stock PushBlock.onnx. This is the original discrete PushBlock, not the hybrid PushBlockWithInput.
2. Preserve the two 105-d sensors and the single Discrete(7) action branch.
3. Stop at one million transitions and package each physical-agent episode independently.

Expert validation: 53,039 episodes (52,946 terminated / 93 truncated at the recording boundary), mean return 4.972780, mean ep length 18.85.

Crawler, Pyramids, Worm, Walker, 3DBallHard

Use each model-removed build with its matching stock ONNX; record each physical scene agent independently and stop at one million transitions. Schemas come directly from the live ML-Agents behavior specs.

• Crawler — 1,043 episodes, 1,000,000 transitions, mean return 2575.8405, mean length 958.77 (76 terminated / 967 truncated). Tuple(Box(126), Box(32)), Box(20).
• Pyramids — 5,392 episodes, 1,000,000 transitions, mean return 1.797730, mean length 185.46 (5,344 terminated / 48 truncated). Tuple(Box(56)×3, Box(4)), Discrete(5).
• Worm — 1,000 episodes, 1,000,000 transitions, mean return 1044.106317, mean length 1000.00 (0 terminated / 1,000 truncated). Box(64), Box(9).
• Walker — 1,464 episodes, 1,000,000 transitions, mean return 1354.565443, mean length 683.06 (768 terminated / 696 truncated). Box(243), Box(39).
• 3DBallHard — 1,011 episodes, 1,000,008 transitions, mean return 98.909511, mean length 989.13 (3 terminated / 1,008 truncated). Tuple(Box(27), Box(18)), Box(2).

The recipe ends at the Minari dataset — a portable, env-less trajectory set.