1. Improved POMDP Tree Search Planning with Prioritized Action Branching
- Author
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Mern, John, Yildiz, Anil, Bush, Larry, Mukerji, Tapan, and Kochenderfer, Mykel J.
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,I.2.8 ,General Medicine ,Machine Learning (cs.LG) - 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., Comment: 7 pages
- Published
- 2021