ICML 2022spotlight43 citations
Individual Reward Assisted Multi-Agent Reinforcement Learning
Li Wang, Yupeng Zhang, Yujing Hu, Weixun Wang, Chongjie Zhang, Yang Gao, Jianye Hao, Tangjie Lv
Abstract
In many real-world multi-agent systems, the sparsity of team rewards often makes it difficult for an algorithm to successfully learn a cooperative team policy. At present, the common way for solving this problem is to design some dense individual rewards for the agents to guide the cooperation. However, most existing works utilize individual rewards in ways that do not always promote teamwork and sometimes are even counterproductive. In this paper, we propose
BibTeX
@InProceedings{pmlr-v162-wang22ao,
title = {Individual Reward Assisted Multi-Agent Reinforcement Learning},
author = {Wang, Li and Zhang, Yupeng and Hu, Yujing and Wang, Weixun and Zhang, Chongjie and Gao, Yang and Hao, Jianye and Lv, Tangjie and Fan, Changjie},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {23417--23432},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/wang22ao/wang22ao.pdf},
url = {https://proceedings.mlr.press/v162/wang22ao.html},
abstract = {In many real-world multi-agent systems, the sparsity of team rewards often makes it difficult for an algorithm to successfully learn a cooperative team policy. At present, the common way for solving this problem is to design some dense individual rewards for the agents to guide the cooperation. However, most existing works utilize individual rewards in ways that do not always promote teamwork and sometimes are even counterproductive. In this paper, we propose