Multi-Agent Planning with Baseline Regret Minimization
Abstract
We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably better than or at least equivalent to the baseline policy. We also propose an iterative belief generation algorithm to effectively and efficiently minimize the baseline regret, which only requires necessary iterations to converge to the policy with minimum baseline regret. Experimental results on common benchmark problems confirm its advantage comparing to the state-of-the-art approaches.
Cite
Text
Wu et al. "Multi-Agent Planning with Baseline Regret Minimization." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/63Markdown
[Wu et al. "Multi-Agent Planning with Baseline Regret Minimization." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/wu2017ijcai-multi/) doi:10.24963/IJCAI.2017/63BibTeX
@inproceedings{wu2017ijcai-multi,
title = {{Multi-Agent Planning with Baseline Regret Minimization}},
author = {Wu, Feng and Zilberstein, Shlomo and Chen, Xiaoping},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {444-450},
doi = {10.24963/IJCAI.2017/63},
url = {https://mlanthology.org/ijcai/2017/wu2017ijcai-multi/}
}