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Improved POMDP Tree Search Planning with Prioritized Action Branching
- Source :
- Proceedings of the AAAI Conference on Artificial Intelligence. 35:11888-11894
- Publication Year :
- 2021
- Publisher :
- Association for the Advancement of Artificial Intelligence (AAAI), 2021.
-
Abstract
- Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sample a subset of the action space that provides varying mixtures of exploitation and exploration for inclusion in a search tree. The proposed method first evaluates the action space according to a score function that is a linear combination of expected reward and expected information gain. The actions with the highest score are then added to the search tree during tree expansion. Experiments show that PA-POMCPOW is able to outperform existing state-of-the-art solvers on problems with large discrete action spaces.<br />Comment: 7 pages
Details
- ISSN :
- 23743468 and 21595399
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Proceedings of the AAAI Conference on Artificial Intelligence
- Accession number :
- edsair.doi.dedup.....471b85fb963ca277a2750ac137c12a97