A Finite-State Controller Based Offline Solver for Deterministic POMDPs

Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.

@article{schutz2025_2505.00596,
  title={ A Finite-State Controller Based Offline Solver for Deterministic POMDPs },
  author={ Alex Schutz and Yang You and Matias Mattamala and Ipek Caliskanelli and Bruno Lacerda and Nick Hawes },
  journal={arXiv preprint arXiv:2505.00596},
  year={ 2025 }
}