Methods › General › Self-Supervised Learning › IMGEP
Intrinsically Motivated Goal Exploration Processes
IMGEP
Introduced by Sébastien Forestier et al. in Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Population-based intrinsically motivated goal exploration algorithms applied to real world robot learning of complex skills like tool use.
Papers archive 2025-07-28
2 shown of 2, 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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First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation 6 Dec 2022 · 0 repositories · arXiv:2212.03251
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Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning 7 Aug 2017 · 3 repositories · arXiv:1708.02190
Tasks archive 2025-07-28
8 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 |
|---|---|
| Continuous Control | 1 |
| Developmental Learning | 1 |
| MuJoCo | 1 |
| Multi-Goal Reinforcement Learning | 1 |
| Reinforcement Learning | 1 |
| Reinforcement Learning (RL) | 1 |
| Self-Supervised Learning | 1 |
| continuous-control | 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
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