The DOI matches its Crossref record.
Adaptive aggregation methods for infinite horizon dynamic programming
Open accessAbstract
A class of iterative aggregation algorithms for solving infinite horizon dynamic programming problems is proposed. The idea is to interject aggregation iterations in the course of the usual successive approximation method. An important feature that sets this method apart from earlier ones is that the aggregate groups of states change adaptively from one aggregation iteration to the next, depending on the progress of the computation. This allows acceleration of convergence in difficult problems involving multiple-ergodic classes for which methods using fixed groups of aggregate states are ineffective. No knowledge of special problem structure is utilized by the algorithms.>
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
Boutilier, C., Dean, T., & Hanks, S. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.575 · Cited by 1,074
Feature-based methods for large scale dynamic programming
Tsitsiklis, J. N., & van Roy, B. (1996) · Machine Learning · doi:10.1007/bf00114724 · Cited by 432
Stochastic dynamic programming with factored representations
Boutilier, C., Dearden, R., & Goldszmidt, M. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(00)00033-3 · Cited by 361
Metadata
- Authors
- Bertsekas, D. P., & Castanon, D. A.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1989-06
- Volume
- 34
- Issue
- 6
- Pages
- 589-598
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2101167844
- Cited by
- 171
- References
- 0
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Adaptive aggregation methods for infinite horizon dynamic programming. https://app.cochat.ai/papers/10.1109/9.24227
- The paper
- Bertsekas, D. P., & Castanon, D. A. (1989). Adaptive aggregation methods for infinite horizon dynamic programming. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.24227
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
