The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Feature-based methods for large scale dynamic programming
Open accessAbstract
We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations; that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. As an example, one of these algorithms is used to generate a strategy for the game of Tetris. Furthermore, we provide a counter-example illustrating the difficulties of integrating compact representations with dynamic programming, which exemplifies the shortcomings of certain simple approaches.
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Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Reinforcement Learning: A Survey
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Algorithms for Reinforcement Learning
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Metadata
- Authors
- Tsitsiklis, J. N., & van Roy, B.
- Venue
- Machine Learning
- Published
- 1996-03
- Volume
- 22
- Issue
- 1-3
- Pages
- 59-94
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2169982856
- Cited by
- 432
- References
- 20
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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