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State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms
Abstract
This paper surveys models and algorithms dealing with partially observable Markov decision processes. A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process which permits uncertainty regarding the state of a Markov process and allows for state information acquisition. A general framework for finite state and action POMDP's is presented. Next, there is a brief discussion of the development of POMDP's and their relationship with other decision processes. A wide range of models in such areas as quality control, machine maintenance, internal auditing, learning, and optimal stopping are discussed within the POMDP-framework. Lastly, algorithms for computing optimal solutions to POMDP's are presented.
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Cited by
Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00023-x · Cited by 4,149
Resource Management with Deep Reinforcement Learning
Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353
A survey of algorithmic methods for partially observed Markov decision processes
Lovejoy, W. S. (1991) · Annals of Operations Research · doi:10.1007/bf02055574 · Cited by 532
Robust Markov Decision Processes
Wiesemann, W., Kuhn, D., & Rustem, B. (2013) · Mathematics of Operations Research · doi:10.1287/moor.1120.0566 · Cited by 312
Cognitive Radio Techniques Under Practical Imperfections: A Survey
Sharma, S. K., Bogale, T. E., Chatzinotas, S., Ottersten, B., Le, L. B., & Wang, X. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2452414 · Cited by 221
Metadata
- Authors
- Monahan, G. E.
- Venue
- Management Science
- Published
- 1982-01
- Volume
- 28
- Issue
- 1
- Pages
- 1-16
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2123651102
- Cited by
- 745
- References
- 0
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Monahan, G. E. (1982). State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms. Management Science. https://doi.org/10.1287/mnsc.28.1.1
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
