ICML 2022spotlight77 citations
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar
Abstract
Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential sample complexity dependence on the number of agents, a phenomenon known as
BibTeX
@InProceedings{pmlr-v162-mao22a,
title = {On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning},
author = {Mao, Weichao and Yang, Lin and Zhang, Kaiqing and Basar, Tamer},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {15007--15049},
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/mao22a/mao22a.pdf},
url = {https://proceedings.mlr.press/v162/mao22a.html},
abstract = {Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential sample complexity dependence on the number of agents, a phenomenon known as