The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Simulation-based optimization of Markov reward processes
Open accessAbstract
This paper proposes a simulation-based algorithm for optimizing the average reward in a finite-state Markov reward process that depends on a set of parameters. As a special case, the method applies to Markov decision processes where optimization takes place within a parametrized set of policies. The algorithm relies on the regenerative structure of finite-state Markov processes, involves the simulation of a single sample path, and can be implemented online. A convergence result (with probability 1) is provided.
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Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Cited by
Konda, V. R., & Tsitsiklis, J. N. (2003) · SIAM Journal on Control and Optimization · doi:10.1137/s0363012901385691 · Cited by 709
Self-citationNatural actor–critic algorithms
Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M., & Lee, M. (2009) · Automatica · doi:10.1016/j.automatica.2009.07.008 · Cited by 559
Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC*
Bertsekas, D. P. (2005) · European Journal of Control · doi:10.3166/ejc.11.310-334 · Cited by 366
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Greensmith, E., Bartlett, P. L., & Baxter, J. (2004) · doi:10.5555/1005332.1044710 · Cited by 300
Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000570 · Cited by 257
Metadata
- Authors
- Marbach, P., & Tsitsiklis, J. N.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2001
- Volume
- 46
- Issue
- 2
- Pages
- 191-209
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2120465407
- Cited by
- 306
- References
- 2
- FWCI
- 28.79
- Type
- journal article
- Language
- en
- Open access
- green
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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.
