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| 001 | 1006386 | ||
| 005 | 20251006110519.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.48550/arXiv.2412.08574 |
| 024 | 7 | _ | |2 arXiv |a arXiv:2412.08574 |
| 024 | 7 | _ | |2 datacite_doi |a 10.18154/RWTH-2025-02461 |
| 037 | _ | _ | |a RWTH-2025-02461 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-82)1010456 |a Aichmüller, Michael |b 0 |u rwth |
| 111 | 2 | _ | |a 39. Annual AAAI Conference on Artificial Intelligence |c Philadelphia, PA |d 2025-02-27 - 2025-03-04 |g AAAI 2025 |w USA |
| 245 | _ | _ | |a Learning Sketch Decompositions in Planning via Deep Reinforcement Learning |h online |
| 260 | _ | _ | |c 2024 |
| 300 | _ | _ | |a 35 Seiten |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
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| 536 | _ | _ | |0 G:(EU-Grant)885107 |a RLeap - From Data-based to Model-based AI: Representation Learning for Planning (885107) |c 885107 |f ERC-2019-ADG |x 1 |
| 591 | _ | _ | |a Germany |
| 700 | 1 | _ | |0 P:(DE-82)IDM06496 |a Geffner, Hector |b 1 |u rwth |
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