Improved POMDP Tree Search Planning with Prioritized Action Branching

• URL: http://arxiv.org/abs/2010.03599v1
• Date: Wed, 7 Oct 2020 18:33:57 GMT
• Title: Improved POMDP Tree Search Planning with Prioritized Action Branching
• Authors: John Mern, Anil Yildiz, Larry Bush, Tapan Mukerji, Mykel J. Kochenderfer
• Abstract summary: 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. Experiments show that PA-POMCPOW is able to outperform existing state-of-the-art solvers on problems with large discrete action spaces.
• Score: 33.94599291823342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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