Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning
Abstract
We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphsâthose that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discovery method allows it to successfully identify the type of subgoals we seekâstates that lie between two densely-connected regions of the state spaceâwhile producing an algorithm with low computational cost.
Cite
Text
Simsek et al. "Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102454Markdown
[Simsek et al. "Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/simsek2005icml-identifying/) doi:10.1145/1102351.1102454BibTeX
@inproceedings{simsek2005icml-identifying,
title = {{Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning}},
author = {Simsek, Özgür and Wolfe, Alicia P. and Barto, Andrew G.},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {816-823},
doi = {10.1145/1102351.1102454},
url = {https://mlanthology.org/icml/2005/simsek2005icml-identifying/}
}