Abstract
How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time. To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator. This allows us to use the approximate simulator to replace the original simulator for faster simulations when it is accurate enough under the current context, thus trading off simulation speed and accuracy. Experimental results in two large domains show that when integrated with POMCP, our approach allows to plan with improving efficiency over time.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence |
| Editors | Luc De Raedt |
| Publisher | International Joint Conferences on Artificial Intelligence (IJCAI) |
| Pages | 4628-4634 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-956792-00-3 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.