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.
@article{arxiv.2010.03599,
title = {Improved POMDP Tree Search Planning with Prioritized Action Branching},
author = {John Mern and Anil Yildiz and Larry Bush and Tapan Mukerji and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:2010.03599},
year = {2021}
}