Efficient Hierarchical MCMC for Policy Search
Abstract
Many inference and optimization tasks in machine learning can be solved bysampling approaches such as Markov Chain Monte Carlo (MCMC) and simulatedannealing. These methods can be slow if a single target density query requiresmany runs of a simulation (or a complete sweep of a training data set). Weintroduce a hierarchy of MCMC samplers that allow most steps to be taken inthe solution space using only a small sample of simulation runs (or trainingexamples). This is shown to accelerate learning in a policy searchoptimization task.
Cite
Text
Strens. "Efficient Hierarchical MCMC for Policy Search." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015381Markdown
[Strens. "Efficient Hierarchical MCMC for Policy Search." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/strens2004icml-efficient/) doi:10.1145/1015330.1015381BibTeX
@inproceedings{strens2004icml-efficient,
title = {{Efficient Hierarchical MCMC for Policy Search}},
author = {Strens, Malcolm J. A.},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015381},
url = {https://mlanthology.org/icml/2004/strens2004icml-efficient/}
}