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ProcGen

Introduced by Karl Cobbe et al. in Leveraging Procedural Generation to Benchmark Reinforcement Learning3 Dec 2019 archive 2025-07-28

Procgen Benchmark includes 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Reinforcement Learning (RL) ProcGen PPG Mean Normalized Performance 0.757 Phasic Policy Gradient opendilab/DI-engine +2 2 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 177. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Phasic Policy Gradient 3 2 9 Sep 2020 ran 0 of 13 samples (13 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • ProcGen

1 variant name, as the archive lists them.

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