Methods › Reinforcement Learning › Distributed Reinforcement Learning › APPO
Asynchronous Proximal Policy Optimization
APPO
Introduced by Aleksei Petrenko et al. in Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The archive carries no description for this method.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning 7 Mar 2025 · 1 repository · arXiv:2503.05306Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
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StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models 10 Oct 2024 · 1 repository · arXiv:2410.07652Syntology ran 4 of 9 samples · 5 unverified · 9 pointer-only (licence)
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RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing 5 Oct 2023 · 1 repository · arXiv:2310.03507
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Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning 21 Jun 2020 · 4 repositories · arXiv:2006.11751Syntology ran 0 of 5 samples · 5 unverified
Tasks archive 2025-07-28
15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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