PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
Abstract
We present PolicyBlocks, an algorithm by which a reinforcement learning agent can extract useful macro-actions from a set of related tasks. The agent creates macroactions by finding commonalities in solutions to previous tasks. Using these macro-actions, learning to do future related tasks is accelerated.
Cite
Text
Pickett and Barto. "PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning." International Conference on Machine Learning, 2002.Markdown
[Pickett and Barto. "PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning." International Conference on Machine Learning, 2002.](https://mlanthology.org/icml/2002/pickett2002icml-policyblocks/)BibTeX
@inproceedings{pickett2002icml-policyblocks,
title = {{PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning}},
author = {Pickett, Marc and Barto, Andrew G.},
booktitle = {International Conference on Machine Learning},
year = {2002},
pages = {506-513},
url = {https://mlanthology.org/icml/2002/pickett2002icml-policyblocks/}
}