Population-Based Diverse Exploration for Sparse-Reward Multi-Agent Tasks
Abstract
We consider strategy proof mechanisms for facility location which maximize equitability between agents. As is common in the literature, we measure equitability with the Gini index. We first prove a simple but fundamental impossibility result that no strategy proof mechanism can bound the approximation ratio of the optimal Gini index of utilities for one or more facilities. We propose instead computing approximation ratios of the complemented Gini index of utilities, and consider how well both deterministic and randomized mechanisms approximate this. In addition, as Nash welfare is often put forwards as an equitable compromise between egalitarain and utilitarian outcomes, we consider how well mechanisms approximate the Nash welfare.
Cite
Text
Xu et al. "Population-Based Diverse Exploration for Sparse-Reward Multi-Agent Tasks." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/32Markdown
[Xu et al. "Population-Based Diverse Exploration for Sparse-Reward Multi-Agent Tasks." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/xu2024ijcai-population/) doi:10.24963/ijcai.2024/32BibTeX
@inproceedings{xu2024ijcai-population,
title = {{Population-Based Diverse Exploration for Sparse-Reward Multi-Agent Tasks}},
author = {Xu, Pei and Zhang, Junge and Huang, Kaiqi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {283-291},
doi = {10.24963/ijcai.2024/32},
url = {https://mlanthology.org/ijcai/2024/xu2024ijcai-population/}
}