Learning Sequential Decision Rules Using Simulation Models and Competition
Abstract
The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested.
Cite
Text
Grefenstette et al. "Learning Sequential Decision Rules Using Simulation Models and Competition." Machine Learning, 1990. doi:10.1007/BF00116876Markdown
[Grefenstette et al. "Learning Sequential Decision Rules Using Simulation Models and Competition." Machine Learning, 1990.](https://mlanthology.org/mlj/1990/grefenstette1990mlj-learning/) doi:10.1007/BF00116876BibTeX
@article{grefenstette1990mlj-learning,
title = {{Learning Sequential Decision Rules Using Simulation Models and Competition}},
author = {Grefenstette, John J. and Ramsey, Connie Loggia and Schultz, Alan C.},
journal = {Machine Learning},
year = {1990},
pages = {355-381},
doi = {10.1007/BF00116876},
volume = {5},
url = {https://mlanthology.org/mlj/1990/grefenstette1990mlj-learning/}
}