The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces
Abstract
Parti-game is a new algorithm for learning from delayed rewards in high dimensional real-valued state-spaces. In high dimensions it is essential that learning does not explore or plan over state space uniformly. Part i-game maintains a decision-tree partitioning of state-space and applies game-theory and computational geom(cid:173) etry techniques to efficiently and reactively concentrate high reso(cid:173) lution only on critical areas. Many simulated problems have been tested, ranging from 2-dimensional to 9-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and uncurl(cid:173) ing snake robots in restricted spaces. In all cases, a good solution is found in less than twenty trials and a few minutes.
Cite
Text
Moore. "The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces." Neural Information Processing Systems, 1993.Markdown
[Moore. "The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/moore1993neurips-partigame/)BibTeX
@inproceedings{moore1993neurips-partigame,
title = {{The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces}},
author = {Moore, Andrew W.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {711-718},
url = {https://mlanthology.org/neurips/1993/moore1993neurips-partigame/}
}