Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided uniform sam...
Main Authors: | Kaelbling, Leslie P., Lozano-Pérez, Tomás, Kim, Beomjoon |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
2021
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Online Access: | https://hdl.handle.net/1721.1/137707 |
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