Commit History

10 days ago

  1. Add verified PushBlockWithInput trajectories (three quality tiers)

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  2. Add verified FoodCollector trajectories (three quality tiers)

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  3. Add verified Sorter trajectories (three quality tiers)

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  4. Publish validated WallJump expert medium and simple trajectories

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  5. Exclude mutable Unity timer logs from StrikersVsGoalie input checksums

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  6. Pin PushBlockCollab reproduction code commit

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  7. Document validated PushBlockCollab quality ladder

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  8. Add validated PushBlockCollab medium trajectories

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  9. Fix dataset card rendering by normalizing duplicate CR line endings

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  10. Add validated PushBlockCollab simple trajectories

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  11. Add validated PushBlockCollab expert trajectories

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  12. Add validated StrikersVsGoalie stock-policy datasets (three tiers, two roles)

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    153c896

  1. docs: one row per environment instead of one per tier⁠

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  2. docs: put the download size next to the download⁠

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  1. datasets: drop local filesystem paths from collection_spec provenance

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  1. card: link the collection recipe (examples/unity_collection) in Companion repos

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  1. Link the GitHub collection/ packager from the download path

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  1. Correct companion policy-repo environments to DungeonEscape and SoccerTwos

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  2. docs: quantify run-to-run precision; state the ladder count plainly⁠

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  1. docs: link the two language editions and correct the checkpoint list⁠

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  2. docs(ko): bring the Korean card to full parity with the English one⁠

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  3. docs: document the episode-length distribution of every tier⁠

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  4. docs: carry the re-recorded continuous figures and close the action-label notice⁠

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  5. fix(3dball-hard): re-record all three tiers with the decision-gap action-label fix⁠

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  6. fix(walker): re-record all three tiers with the decision-gap action-label fix⁠

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  7. fix(crawler): re-record all three tiers with the decision-gap action-label fix⁠

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  8. card: state the continuous scenes as published, with measured placeholder shares⁠

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  9. docs: add Crawler to the reproduction recipe⁠

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  10. card: correct the realized tier-norm ranges and the pending-scene count⁠

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  11. docs: sync soccer-twos and dungeon-escape tier figures to the re-recorded datasets⁠

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  1. card: all 12 discrete-env datasets re-recorded for the action-label gap; refresh counts and ladder figures

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  2. pyramids/simple-v0: re-record with the action-label gap fix (2.98% of rows held a placeholder 0); band U(4.8,7.1) unchanged

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  3. pyramids/medium-v0: re-record with the action-label gap fix (4.46% of rows held a placeholder 0); band U(2.7,4.9) unchanged

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  4. pushblock/simple-v0: re-record with the action-label gap fix (46.6% of rows held a placeholder 0); band U(3.25,4.75) unchanged

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  5. pushblock/medium-v0: re-record with the action-label gap fix (61.0% of rows held a placeholder 0); band U(2.1,3.2) unchanged

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  6. dungeon-escape/simple-v0: restore the band-qualified policy_id (stock-r23-group-temperature-range-3.0-4.0) dropped in the action-label re-record

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  7. dungeon-escape/medium-v0: restore the band-qualified policy_id (stock-r23-group-temperature-range-1.5-3.0) dropped in the action-label re-record

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  8. soccer-twos/simple-v0: re-record with the action-label gap fix (6.4% of rows were a placeholder [0,0,0]); per-team band U(2.1,3.4) and side-swap norm ~0.60 unchanged

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  9. soccer-twos/medium-v0: re-record with the action-label gap fix (6.4% of rows were a placeholder [0,0,0]); per-team band U(1.3,2.6) and side-swap norm ~0.80 unchanged

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  10. dungeon-escape/simple-v0: re-record with the action-label gap fix (action 0 share 32.49% -> 11.05%); band U(3.0,4.0) and norm 0.5990 unchanged

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  11. dungeon-escape/medium-v0: re-record with the action-label gap fix (action 0 share 36.93% -> 9.53%); band U(1.5,3.0) and norm 0.8007 unchanged

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  12. docs: pyramids + soccer-twos expert validation numbers after re-record

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  13. card: pyramids + soccer-twos expert-v0 re-record (all four discrete experts now fixed)

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  14. soccer-twos/expert-v0: fix action labels — the collector recorded a fabricated (0,0,0) for every step an agent spent inside ML-Agents' action-repeat gap (11.19% of rows held (0,0,0) where the policy's joint probability for it is 3.80%); it now carries the last real decision forward, so the action column matches the policy the trajectories were actually driven by

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  15. pyramids/expert-v0: fix action labels — the collector recorded a fabricated 0 for every step an agent spent inside ML-Agents' action-repeat gap (~6% of rows); it now carries the last real decision forward, so the action column matches the policy the trajectories were actually driven by

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  16. docs: dungeon-escape expert validation numbers after re-record

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  17. card: dungeon-escape expert-v0 re-record (35,505 ep, anchor 0.019355, medium norm 0.81)

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  18. dungeon-escape/expert-v0: fix action labels — the collector recorded a fabricated 0 for every step an agent spent inside ML-Agents' action-repeat gap (41.5% of rows); it now carries the last real decision forward, so the action column matches the policy the trajectories were actually driven by

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  19. docs: pushblock expert re-record numbers + decision-period action-label rule

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  20. card: pushblock expert-v0 re-record (53,039 ep, anchor 0.353699) + action-label data-quality notice

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