Corpus ID: 198179734

Multilevel Monte-Carlo for Solving POMDPs Online

@article{Hrger2019MultilevelMF,
  title={Multilevel Monte-Carlo for Solving POMDPs Online},
  author={Marcus H{\"o}rger and H. Kurniawati and A. Elfes},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.09673}
}
Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remain challenging. Most on-line solvers rely on a large number of forward… Expand
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