Partially Observable Markov Decision Process (POMDP). The ’pomdp-solve’ program solves problems that are formulated as partially observable Markov decision processes, a.k.a. POMDPs. It uses the basic dynamic programming approach for all algorithms, solving one stage at a time working backwards in time. It does finite horizon problems with or without discounting. It will stop solving if the answer is within a tolerable range of the infinite horizon answer, and there are a couple of different stopping conditions (requires a discount factor less than 1.0). Alternatively you can solve a finite horizon problem for some fixed horizon length. The code actually implements a number of POMDP solution algorithms

References in zbMATH (referenced in 20 articles )

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  1. Treszkai, Laszlo; Belle, Vaishak: A correctness result for synthesizing plans with loops in stochastic domains (2020)
  2. Kıvanç, İpek; Özgür-Ünlüakın, Demet: An effective maintenance policy for a multi-component dynamic system using factored POMDPs (2019)
  3. Norman, Gethin; Parker, David; Zou, Xueyi: Verification and control of partially observable probabilistic systems (2017)
  4. Chatterjee, Krishnendu; Chmelík, Martin; Tracol, Mathieu: What is decidable about partially observable Markov decision processes with (\omega)-regular objectives (2016)
  5. Seo, Junyeong; Sung, Youngchul; Lee, Gilwon; Kim, Donggun: Training beam sequence design for millimeter-wave MIMO systems: a POMDP framework (2016)
  6. Ayer, Turgay: Inverse optimization for assessing emerging technologies in breast cancer screening (2015)
  7. Chatterjee, Krishnendu; Chmelík, Martin: POMDPs under probabilistic semantics (2015)
  8. Norman, Gethin; Parker, David; Zou, Xueyi: Verification and control of partially observable probabilistic real-time systems (2015)
  9. Gouberman, Alexander; Siegle, Markus: Markov reward models and Markov decision processes in discrete and continuous time: performance evaluation and optimization (2014)
  10. Li, Yanjie; Yin, Baoqun; Xi, Hongsheng: Finding optimal memoryless policies of POMDPs under the expected average reward criterion (2011)
  11. Eker, Barış; Akın, H. Levent: Using evolution strategies to solve DEC-POMDP problems (2010) ioport
  12. Zhang, Hao: Partially observable Markov decision processes: a geometric technique and analysis (2010)
  13. Busemeyer, Jerome R.; Pleskac, Timothy J.: Theoretical tools for understanding and aiding dynamic decision making (2009)
  14. Baier, Christel; Bertrand, Nathalie; Größer, Marcus: On decision problems for probabilistic Büchi automata (2008)
  15. Fernández, Joaquín L.; Sanz, Rafael; Simmons, Reid G.; Diéguez, Amador R.: Heuristic anytime approaches to stochastic decision processes (2006)
  16. Roy, N.; Gordon, G.; Thrun, S.: Finding approximate POMDP solutions through belief compression (2005)
  17. Spaan, M. T. J.; Vlassis, N.: Perseus: randomized point-based value iteration for POMDPs (2005)
  18. Zhang, Weihong; Zhang, N. L.: Restricted value iteration: theory and algorithms (2005)
  19. Bernstein, Daniel S.; Givan, Robert; Immerman, Neil; Zilberstein, Shlomo: The complexity of decentralized control of Markov decision processes. (2002)
  20. Lusena, C.; Goldsmith, J.; Mundhenk, M.: Nonapproximability results for partially observable Markov decision processes (2001)

Further publications can be found at: http://www.pomdp.org/pomdp/papers/index.shtml