Planning with Abstract Learned Models While Learning Transferable Subtasks
| dc.contributor.author | Winder, John | |
| dc.contributor.author | Milani, Stephanie | |
| dc.contributor.author | Landen, Matthew | |
| dc.contributor.author | Oh, Erebus | |
| dc.contributor.author | Parr, Shane | |
| dc.contributor.author | Squire, Shawn | |
| dc.contributor.author | desJardins, Marie | |
| dc.contributor.author | Matuszek, Cynthia | |
| dc.date.accessioned | 2021-04-09T16:53:58Z | |
| dc.date.available | 2021-04-09T16:53:58Z | |
| dc.date.issued | 2020-03-04 | |
| dc.description | Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 No. 06: AAAI-20 Technical Tracks 6, AAAI Technical Track: Planning, Routing, and Scheduling | en_US |
| dc.description.abstract | We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks. | en_US |
| dc.description.sponsorship | The material presented here is based in part upon work supported by the National Science Foundation under Grant No. IIS-1813223 and Grant No. IIS-1426452, and by DARPA under grants W911NF-15-1-0503 and D15AP00102. | en_US |
| dc.description.uri | https://ojs.aaai.org//index.php/AAAI/article/view/6555 | en_US |
| dc.format.extent | 9 pages | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/m2zhmk-s5t7 | |
| dc.identifier.citation | Winder, J., Milani, S., Landen, M., Oh, E., Parr, S., Squire, S., desJardins, M., & Matuszek, C. (2020). Planning with Abstract Learned Models While Learning Transferable Subtasks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(06), 9992-10000. https://doi.org/10.1609/aaai.v34i06.6555 | en_US |
| dc.identifier.uri | https://doi.org/10.1609/aaai.v34i06.6555 | |
| dc.identifier.uri | http://hdl.handle.net/11603/21314 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAAI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.title | Planning with Abstract Learned Models While Learning Transferable Subtasks | en_US |
| dc.type | Text | en_US |