Approximate Planning for Factored POMDPs using Belief State Simplification
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Abstract (arXiv)
We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.
- 2022Planning in Observable POMDPs in Quasipolynomial Timecited by 25
- 2021Active Inference Tree Search in Large POMDPscited by 2
- 2023Fast exploration and learning of latent graphs with aliased observationscited by 0
- 2023Fast exploration and learning of latent graphs with aliased observations13 Mar 2023
- 2022Planning in Observable POMDPs in Quasipolynomial Time12 Jan 2022
- 2021Active Inference Tree Search in Large POMDPs25 Mar 2021