Batch-iFDD for Representation Expansion in Large MDPs

Alborz Geramifard, Tom Walsh, Nicholas Roy, Jonathan How
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:402-411, 2013.

Abstract

Matching pursuit (MP) methods are a prom- ising class of feature construction algorithms for value function approximation. Yet exist- ing MP methods require creating a pool of potential features, mandating expert knowl- edge or enumeration of a large feature pool, both of which hinder scalability. This pa- per introduces batch incremental feature de- pendency discovery (Batch-iFDD) as an MP method that inherits a provable convergence property. Additionally, Batch-iFDD does not require a large pool of features, leading to lower computational complexity. Empiri- cal policy evaluation results across three do- mains with up to one million states highlight the scalability of Batch-iFDD over the previ- ous state of the art MP algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-geramifard13a, title = {Batch-iFDD for Representation Expansion in Large MDPs}, author = {Geramifard, Alborz and Walsh, Tom and Roy, Nicholas and How, Jonathan}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {402--411}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/geramifard13a/geramifard13a.pdf}, url = {https://proceedings.mlr.press/r11/geramifard13a.html}, abstract = {Matching pursuit (MP) methods are a prom- ising class of feature construction algorithms for value function approximation. Yet exist- ing MP methods require creating a pool of potential features, mandating expert knowl- edge or enumeration of a large feature pool, both of which hinder scalability. This pa- per introduces batch incremental feature de- pendency discovery (Batch-iFDD) as an MP method that inherits a provable convergence property. Additionally, Batch-iFDD does not require a large pool of features, leading to lower computational complexity. Empiri- cal policy evaluation results across three do- mains with up to one million states highlight the scalability of Batch-iFDD over the previ- ous state of the art MP algorithm.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Batch-iFDD for Representation Expansion in Large MDPs %A Alborz Geramifard %A Tom Walsh %A Nicholas Roy %A Jonathan How %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-geramifard13a %I PMLR %P 402--411 %U https://proceedings.mlr.press/r11/geramifard13a.html %V R11 %X Matching pursuit (MP) methods are a prom- ising class of feature construction algorithms for value function approximation. Yet exist- ing MP methods require creating a pool of potential features, mandating expert knowl- edge or enumeration of a large feature pool, both of which hinder scalability. This pa- per introduces batch incremental feature de- pendency discovery (Batch-iFDD) as an MP method that inherits a provable convergence property. Additionally, Batch-iFDD does not require a large pool of features, leading to lower computational complexity. Empiri- cal policy evaluation results across three do- mains with up to one million states highlight the scalability of Batch-iFDD over the previ- ous state of the art MP algorithm. %Z Reissued by PMLR on 04 October 2026.
APA
Geramifard, A., Walsh, T., Roy, N. & How, J.. (2013). Batch-iFDD for Representation Expansion in Large MDPs. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:402-411 Available from https://proceedings.mlr.press/r11/geramifard13a.html. Reissued by PMLR on 04 October 2026.

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