Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains
Abstract
Black-box domains where the successor states generated by applying an action are generated by a completely opaque simulator pose a challenge for domain-independent planning. The main computational bottleneck in search-based planning for such domains is the number of calls to the black-box simulation. We propose a method for significantly reducing the number of calls to the simulator by the search algorithm by detecting and pruning sequences of actions which are dominated by others. We apply our pruning method to Iterated Width and breadth-first search in domain-independent black-box planning for Atari 2600 games, adding our pruning method significantly improves upon the baseline algorithms.
Cite
Text
Jinnai and Fukunaga. "Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11118Markdown
[Jinnai and Fukunaga. "Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/jinnai2017aaai-learning-a/) doi:10.1609/AAAI.V31I1.11118BibTeX
@inproceedings{jinnai2017aaai-learning-a,
title = {{Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains}},
author = {Jinnai, Yuu and Fukunaga, Alex},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4943-4944},
doi = {10.1609/AAAI.V31I1.11118},
url = {https://mlanthology.org/aaai/2017/jinnai2017aaai-learning-a/}
}