Methods › General › Self-Supervised Learning › ALP-GMM
Absolute Learning Progress and Gaussian Mixture Models for Automatic Curriculum Learning
ALP-GMM
Introduced by Rémy Portelas et al. in Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ALP-GMM is is an algorithm that learns to generate a learning curriculum for black box reinforcement learning agents, whereby it sequentially samples parameters controlling a stochastic procedural generation of tasks or environments.
Papers archive 2025-07-28
1 shown of 1, 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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Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments 16 Oct 2019 · 2 repositories · arXiv:1910.07224Syntology ran 0 of 7 samples · 7 unverified
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Deep Reinforcement Learning | 1 |
| Reinforcement Learning | 1 |
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections